Resource-Constrained Federated Continual Learning: What Does Matter?
Authors: Yichen Li
Yuying Wang
Jiahua Dong
Haozhao Wang
Yining Qi
Rui Zhang
Ruixuan Li
Venue: NeurIPS 2025 poster ()
Keywords: Federated Continual Learning
Federated Learning
Primary area: other

Abstract: Federated Continual Learning (FCL) aims to enable sequential privacy-preserving model training on streams of incoming data that vary in edge devices by preserving previous knowledge while adapting to new data. Current FCL literature focuses on restricted data privacy and access to previously seen data while imposing no constraints on the training overhead. This is unreasonable for FCL applications in real-world scenarios, where edge devices are primarily constrained by resources such as storage, computational budget, and label rate. We revisit this problem with a large-scale benchmark and analyze the performance of state-of-the-art FCL approaches under different resource-constrained settings. Various typical FCL techniques and six datasets in two incremental learning scenarios (Class-IL and Domain-IL) are involved in our experiments. Through extensive experiments amounting to a total of over 1,000+ GPU hours, we find that, under limited resource-constrained settings, existing FCL approaches, with no exception, fail to achieve the expected performance. Our conclusions are consistent in the sensitivity analysis. This suggests that most existing FCL methods are particularly too resource-dependent for real-world deployment. Moreover, we study the performance of typical FCL techniques with resource constraints and shed light on future research directions in FCL.

Resource-Constrained Federated Continual Learning: What Does Matter?
Yichen Li Yuying Wang", Jiahua Dong"; Haozhao Haozhao Wang', Yining Qi', Rui Zhang'! Ruixuan Li
School of Computer Science and Technology.
Huazhong University of Science and Technology, Wuhan, China
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates School of Computer Science and Technology, Soochow University, Suzhou, China (yc1i0204, bz_ aaz_wang)@hus .du.ttedddu.eec edu.cn, en, rayteam@yeah.ner
Abstract
Federated Continual Learning (FCL) aims to enable sequential serracy-preservigg model training on streams of incoming data that vary in edge devices by preserving previous knowledge while adapting to new data. Current FCL literature focuses restricted data privacy and access to previously seen data while imposing no constraints on the training overhead. This is unreasonable for FCL applications real-world scenarios, where edge devices such as storage, computational budget, and label rate. We revisit this problem with a large-scale benchmark and analyze the performance of state-of-the-arr FCL approaches under different techniques and six datasets in two incremental learning scenarios (Class-IL and Domain-IL) are involved in our experiments. Through extensive experiments amounting to total of over 1,000+ GPU hours, find that, under limited resource-constrainel settings, existing FCL approaches, with no exception, fail to achieve the expected performance. Our conclusions consistent in the sensitivity analysis. This suggests that most existing FCL methods are particularly too resource-dependent for real-world deployment. Moreover, we study the perfor- mance of typical FCL techniques with resource constraints and shed light on future research directions in FCL.
primarily constrained by resources
settings. Various typical FCL
Introduction
Federated Learning (FL) emerges as a distributed learning paradigm, facilitating the collaborative training of global deep learning model among edge clients while ensuring the privacy of locally stored data [38 54 53 26) Recently, FI. has garnered significant interest and found applications in and smart healthcare solutions
diverse domains, including recommendation systems 61 25 24
55 71.
Typically, FL has been actively studied in static setting, where the number of training samples does not change over time. However, realistic FL application, each client may continue collecting new data and train the local model with streaming tasks, leading performance degradation on previous tasks [60 27 28]. Such phenomenon is known as catastrophic forgetting [9] in the Continual Learning (CL) paradigm. This challenge is further compounded in FL settings, where the local data remains inaccessible to others, and training on clients is constrained by limited resources.
'Jiahua Dong and Ruixuan Li corresponding authors. Homepage: https:///wwt http://wwwwwwwzhaaggcccmm ruizhang...... info/
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Table Primary Directions of Progress in FCL. Analysis of three kinds of classifications in the study of the CL system (replay, regularization, and network), with bold highlighting the main contribution. Besides, we further summarize four typical FCL techniques (Sample Caching, Data Synthesis, Knowledge Distillation, and Network Extension) from existing works and focus two common FCL scenarios. Here "Constraint" denotes the research based on whether the additional resource overheads are required (Memory Buffer, Computational Budget and Label Rate).
Scenurios
Constraints
Typical FCL Techniques
Dir.
Reference
Contribution
Class-II.
Domain-IL.
MB
LR
sc
DS
KD
NE
Centralized
X
X
v
x
X
x
X
FedAvg
x
X
X
FedProx
x
X
5
X
X
X
TARGET
Exemplar Sample
X
4
X
V
X
Replay
FedCIL 45
Generation & Alignment
X
4
V
X
V
X
Re-Fed
23
Synergistic Repluy
4
X
x
X
AF-FCL
58
Accurate Forgetting
X
x
V
X
GLFC 5
Class-Aware Loss
X
V
X
X
X
Regularization
FOT
Orthogonality Projection
CFcD 37
Knowledge Distillation
x
X
X
FedET 35
Pre-training Backhone
X
MFCL
Data-Free Distillation
X
x
x
LGA
4
Category-Aware Loss
x
x
x
Network
FCL-BL
Broad Learning
FedWelT
62
Parameter Decomposition
x
x
x
PFedDHI.
Knowledge Matching
x
x
To address this issue, researchers have studied federated continual learning (FCL), which enables each client to leamn from local private and incremental task stream continuously. One mainstream technique is re-train the model with cached synthetic samples later, a strategy referred to as data replay. FedCIL is proposed in [5] to reconstruct previous samples with a learnable generative network for replay, improving the retention of previous information. The authors in (3 propose to discern important samples from streaming tasks and update the local model with both cached samples and the current task's samples. Another promising solution for FCL limit the update of model parameters to new tasks, a process known as parameter regularization. The works in 5 concentrate on federated class-incremental learning, addressing scenario scenario-specific challenges through the computation of supplementary class-imbalance losses to train unified global model. It studied in [2] that projecting the different tasks' parameters onto orthogonal subspaces prevents
new tasks from overwriting previous task parameters. In addition, the authors in [62 19, [55] extend the backbone model with multi-head classifiers isolate the task-specific parameters prevent
forgetting.
However, the current FCL. literature overlooks key necessity for practical real deployment of such algorithms. In particular, most existing methods focus on offline federated continual learning where, despite limited access to previous data, training algorithms do not have restrictions on the training resources. For example, Snapchat [68], in 2017, reported an influx of over 3.5 billion short videos daily from users across the globe. These videos had be instantly processed for various tasks, from image rating and recommendation to hate speech and misinformation detection. Otherwise, new streaming data will accumulate until training is completed, causing server delays and worsening user experience. Different from the conditions in the centralized server, distributed devices (usually mobile or edge devices) pose greater challenges to the training resources. this paper, we mainly focus on the following three mainstream resources, which are expensive on distributed devices.
Memory Buffer. Memory buffer serves crucial training resource for many existing methods, particularly those based on replay, as they necessitate the storage of previous samples or synthetic data for retraining. Additionally, some other methods relying on regularization also require buffering a portion of sample data or utilizing extra auxiliary datasets to facilitate the training process. In recent years, despite the advanced maturity of current storage technology, numerous platforms possess vast storage capacities, exemplified by computer SSDs priced at roughly 0.061S/GB in the market"
Price reference for PC: http://www..mmacc.ccoo/ss//////s

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Nevertheless, when it comes to distributed devices, augmenting storage capacity remains costly, as seen in the iPhone's storage price, hovering around 0.487S/GB nearly eightfold the price of Therefore, a thorough examination of the memory buffer overhead demanded by existing FCL methods is imperative.
computer S [
Computational Budget. The computational budgets of continual learning (CL) algorithms tend to exceed those of static data-based methods, as the model must converge on each incremental task prior to learning new ones. Amidst the increasing influx of streaming data, it is paramount for each client to optimize their computational efficiency. However, distributed devices (e.g.. IoT and mobile devices) are usually equipped with portable hardware and sacrifice powerful computing resources. A feasible way for distributed devices to improve computational efficiency to rent cloud platform computing resources, but this is expensive. According to A4, executing a CL algorithm on the CLEAR benchmark [34, which performs 300,000 iterations, incurs approximately a cost of 100$ on an A A10 Google instance, equating to 3$ per hour for single GPU. However, in existing methods, such as those based on replay, additional computational budgets are required for retraining the cached samples training generative models to produce pscudo-data. On the other hand, regularization- based methods may necessitate extra computation for knowledge distillation. In FCL scenarios, limiting the computational budget is necessary for reducing the overall cost.
Label Rate. Additionally, the majority of existing works assume that a full set of labels accessible. Only recently, some work in the centralized server started to consider budgeted continual learning, which aims to ensure the applicabilitty of continual learning algorithms under real-world scenarios. Nevertheless, existing FCL methods still concentrate on fully labeled data stream, while the data may be presented in an unlabeled form, and the human annotation budget is always large. Although existing FCL methods cannot be trained directly on unlabeled datasets, it is meaningful to appropriately reduce the label rate to observe the impact of the number of training samples on the performance of these methods.
This raises the question: "Do existing federated continual learning algorithmss perform well under constriicen settings?" To address this question, we exhaustively study federated continual learning systems, analyzing the effect of the primary directions of progress proposed in the literature in the setting where algorithms are permitted to limit all three training resources. We evaluate and benchmark at scale various existing FCL. methods and summarize four typical FCL techniques (Sample Caching, Data Synthesis, Knowledge Distillation, and Network Extension) that are common in the literature. Evaluation is carried out on six large-scale datasets in two scenarios (Class-IL. and Domain-IL), amounting to a total of 1,000+ GPU hours under various settings. As shown in Table [ we conclude the existing FCL methods and analyze their main contributions and typical techniques with training resources involved. We summarize our empirical conclusions in three folds:
First, we are the first to revisit resource constraints in federated continual learning and analyze how all three training resources (memory buffer, computational budget, and label rate data) can pose great challenge to the federated continual learning issue.
Then, we conduct extensive experiments on more than existing FCL algorithms with different limited resources and find that existing FCL literature is particularly suited for settings where memory is limited and less practical in scenarios having limited computational budgets and sparsely labeled data.
Finally, we analyze the effect of typical FCL techniques with resource constraints and discuss the future research directions of federated continual learning with resource constraints.
2 Dissecting Federated Continual Learning Systems
Federated continual learning methods typically propose system of multiple components that jointly help improve learning performance. The problem formulation is illustrated in Appendix [B] In this section, we analyze FCL systems and dissect them into their underlying techniques. This helps to analyze and isolate the role of different components with different techniques under our cnouure-constrained settings and helps us to understand the most significant techniques. Overall, we summarize four major techniques from main contributions in Table
'Price reference for Phone: http::/wwww..mpp..cmmmmshhpppppp
3

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(I) Sample Caching. Rehearsing samples from previous tasks is a basic approach in FCL. Particularly when access to previous samples is restricted a small memory, they are used to select which samples from the stream will update the memory. Recent FCL. works in [23 29 propose to synergistically cache samples based on both the local and global understanding. Other methods have not considered the sample selection strategy in FCL. For a fair comparison in our constrained memory buffer setup, we employ random sampling equally to corresponding methods.
(2) Data Synthesis. Considering the security and privacy of data in some scenarios, or data protection under GDPR, the data of previous tasks cannot be cached locally. Some studies use generative models to learn the distribution of previous tasks and generate synthetic data for data replay. The authors
in [45 66 employ the GAN model [42] generate synthetic samples while [58] explore the NF model 48 to learn the data distribution accurately. In our setting, the synthetic data will also occupy
the memory buffer and the additional training cost brought by the generative model will share the computational budget with the target model.
(3) Knowledge Distillation. As a popular approach, knowledge distillation preserves model perfor- mance on previous tasks by distilling the knowledge from the old model (teacher) to the new model (student). In the FCL, the distillation can be done on either the client side or the server side. On the client side, the student model can be the current model, while the teacher model is the one that has been trained for many previous tasks. On the server side, the student model is always the global model and the teacher model can be denoted as the ensemble prediction by each participating client. In this paper, we only focus on the distillation done on the client side since the resources of the server are not so scarce. The distillation will bring an extra computational budget compared with the basic FedAvg. Moreover, additional distillation data used on the client side will also be included in the memory buffer overhead.
(4) Network Extension. Network extension strategies have been used for two objectives. On the one hand, it has been hypothesized that the large difference in the magnitudes of the weights associated with different classes in the last fully connected layer is among the key reasons behind catastrophic forgetting. There has been family of different methods addressing this problem 622 35 2 On the other hand, several works attempt adapt the model architecture according to the data. This is done by only training part of the model or by directly expanding the model when data is presented [16 65]. However, most of the previous techniques in this area not apply to our setup. Most of this line of work 62 33 2) assumes task misk-incremental setting, where test samples are known to what set of tasks they belong with known boundaries between streaming tasks. For a fair comparison, we modify these methods to make correct classification among all classes from streaming tasks.
3 Experiments
We first start by detailing the experimental setup, datasets, three training resources, and evaluation metrics for our large-scale benchmark. We then present the main results of an evaluation of various FCL methods, followed by an extensive analysis. Our experiments are designed answer the following research questions that are of importance to practical deployment of FCL methods, while also pointing out the future research directions in FCL.
How do resource constraints affect the performance of existing FCL methods? (Section 3.2.1
How do typical techniques studied in FCL literature work under different resource- constrained settings? (Section 3.2.2
3.1 Experiment Setup
3.1.1 Datasets
We conduct our experiments with heterogeneous datasets over two typical scenarios: Class-
Incremental Learning and Domain-Incrementt I Learning on six datasets: CIFAR-10 [18) CIFAR-100
18]. Imay- 20) D0git-10, Office-31 [50) and
69 More details can be
found in Appendix B.I.I

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Table 2: Performance comparison of various methods in two incremental scenarios w.r.t sufficient resources.
CIFAR-10
CIFAR-100
Ting-mmsgeNet
Digi-10
Office111
Officz-Catieb-- 10
Method
AlFi
A
Aifi
A.Fi
Al.)
A(F)
A
Alfi
FollAgg
84
40.944i.
54.33+109
25.14
32.97a112
32.87auu
48.55ac
30.13 in
45.8243.5
47.654044
50.55
55. Maasi
Feilhrx
40 10-00
53.52w01
25.23-371
35.834a
29.03mmm
45. 15
81.35
91.73
46. 14
90wrs
$1,66-1
29
PodAvg+ER 19
42.334154
55.15a129
27.07
37.77a186
33.3
48.50u.
30,45.0.3
46.72aa50
49.50lua2
52.02a51
FedProx-ER
41.00atmn
54.53a05
26.97a1m
36, 10
36,0
dfunee
53.58
TedCIL
45
56.54a125
24.88.144
34.70u1s
36.66
44.54mm31
8Jnes
48.78
50.24
54,89
58.15
TARGET
x
43.78+r
56 34.1
24 01
53, 36
20. 14
45, 15.02am s:
84.73++ 36
93. Teose
48 25
50. 32woss
53.19-a
55, 90
AF-FCL X3
46.98s1.3
57 77
24 62zso
43.58ar 27.15
85 85.90pa:1 e1
93 49ozs
49 19.07
50.87mm
56.12at.
56, 94
Re Fed H
43:48aus
57.52a04
27.56.u31
37.82acm1
35.99
22.19
84.11aa.
94.33m
49.53,x.
52.32
55,08.1.
58.820004
GLFC 1
4
15.02wors
36.60-119
34.7
47.37
79.15
40-46
45.834
47.31
52.37-1
16
FOT
58,24s01s
14.43
38.57ess
34.33
49,52acs
82,97
40-40mos
40.35sa
52.09
52.18
CTeD
46.54a11
58.233136
28.37
35,43am
33,05uee
47.98ursz
s0,23am11
89 1onzz
44,26s.0
46 16.78116
50.13as
S4.89uss
FaiWeIT
12
41.52+1
54 44.5500s
26.03-144
36, 35
34.13
49.24ec as
81.65-
91. 37.33
46.724ast
48.83
51,97-271
56 4040
3.1.2 Baselines
In this paper, we follow the same protocols proposed by 38 47 to set up FII. tasks. We evaluate our resource-constrained settings with twelve baselines: FedAvg (38], FedProx [22). FL+ER 49), FCIL
[5). FedCIL [45), Target (66], AF-FCL [58), Re-Fed [23 FOT 2 and FedWelT 62
More details
about each baseline can be found in Appendix B.1.2
3.1.3 Resource Constraints
We analyze three common constrained resources at edge devices: Memory Buffer, Computational Budget, and Label Rate. Here, abbreviate the memory buffer size M, which means the number of the cached samples in the edge storage, and the computational budget amount as B that denotes the gradient step during the model update; Label Rate R. which represents the ratio of labeled data and unlabeled data. We have adopted different restriction methods for memory buffers and computational budgets for different baselines. The memory buffer is used to cache samples from previous tasks (Re- Fed 231, FCIL(5)), synthetic data (FedCIL(4S). Target[66), AF-FCL((5)) and distillation samples (CFeD(37), ). The computational budget shared by the generative model (FedCIL AF-FCL(58), personalized model (Re-Fed (23), knowledge distillation (FOT(). CFeD (77). The label rate is equally employed all baselines, which controls the ratio of the labeled data.
3.1.4 Configurations
Unless otherwise mentioned, we use ResNet18 [10 as the backbone model for most methods and use the Dirichlet distribution Dir(a) to distribute local samples to stimulate data heterogeneity for all tasks where a smaller indicates higher data heterogeneity. Here we report the final accuracy A() when the model finishes the training the last streaming task and the average accuracy A across all streaming tasks. Although we try to eliminate the gap between baseline methods other than the core algorithm design in the experiment, there are still some methods that directly depend on the backbone model itself. In our experiments, FedCIL (55) employs ACGAN for generative replay, and AF-FCL(58) needs to use a Normalized Flow model to generate the feature space for an accurate forgetting. In addition, we try adopt the ResNet18 to the F but achieve unstable result. According to the available code in the original manuseript, we use AlexNet as the backbone model for the FOT(2. The feasibility of using the ResNet network and achieving excellent performance through simple parameter tuning in the FOT algorithm is still under investigation.
Hardware. Each experiment setting is run twice and we take each run's 's final 10 rounds' accuracy and calculate the average value and standard variance. We use Adam as an optimizer with linear learning rate schedule. We set the remaining parameters according to the values in the original open-source code, such as the weight of multiple distillation losses. All experiments are run on RTX 4090 GPUs and 8*2 RTX 3090 GPUs.
3.2 Overview Performance under Resource Constraints
In this section, we systematically investigate the influence of resource constraints on existing FCL. methods and typical techniques studied in the FCL literature.
5

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Table 3: Performance comparison of various methods in two incremental scenarios w.r.t. extremely
limited resources.
CIFAR 10
CIFAR
100
Thny ImageNet
Digit 10
Office 31
Office Caltech 10
Method
A(5
AU)i
A
AG.)
ALF)
A(.)
A
ALFi
FedAyg
K3
10.1lain
21.76
4,88mm
12.37au
16.1
30.99
72,00euss
80.57:am
8.42a01
8.97aus
9.65aus
10, 33u12
FedProx
10.04a100
21
4,59
11,4a13:
16.92
31.24aa37
71,681144
80,85:ass
8,98
9,45aus
9.37
9.84034
FedCIL
E5
7.08
17.46u151
3,83um
9.26.129
4.13
7.35
51,86u1
62.71uum
5.54ass
7.06une
7.13am
7.70.sm
FOT
8 8.76609
19.574081
4.85.a
11.21-a37
10.444650
49.08
70.96-025
78.13-1sh
8.1740
10.05.0rs
9.88-ass
11 11.24-0
FedWalT
23
9.37
20.52am0
5,95wms
12,32aa
17.32
31 30.00
71.63aum
81,85-1
9.00
9.13a0
8.42am
9,32e1
With Sufficient Resources. In Table 2 we test typical FCL methods under sufficient resource condi- tions. Notably, both FedCIL and AF-FCL methods excel, surpassing most established baselines on CIFAR-10 and D0. This superior performance underscores the effectiveness of their generative models, which can effectively train relatively simple datasets and generate high-quality samples for replay. However, as we delve into more complex and larger-scale datasets like CIFAR- 100 and Tiny-ImageNet, we observe significant drop performance for both FedCIL and AF-FCL. This decline highlights the limitations of their generative models when faced with greater data diversity and complexity. In the context of federated domain-incremenntll learning, where the data distribution changes over time, GLFC demonstrates an interesting behavior. When there are no new incremental sample classes, it reverts to the FedAAg algorithm. On the other hand, Re-Fed consistently achieves reasonable performance across all datasets and scenarios. Its synergistic replay strategy against data heterogeneity seems to provide a robust foundation for FCL.
With Limited Resources. The experiments in Table 3 conducted under extremely limited resource conditions starkly contrast to the results obtained with sufficient resources. By simultaneously restricting three key types of training resources, we observe significant decline in the performance of all methods. This underscores the critical importance of these resources for the effectiveness of federated continual learning techniques. To better understand the impact of these resource constraints, we conducted experiments from two perspectives. First, we analyzed how each resouree limitation affects the performance of the various methods. This allowed us to identify the most sensitive components and bottlenecks that hinder the effectiveness of the FCL techniques under limited conditions. Second, we explored the interactions between different resource constraints and how they compound the challenges faced by the FCL methods. By understanding these interactions, we can gain insights into designing more resource-efficien FCL techniques. A detailed analysis will be provided in the next section.
In summary, the experiments with extremely limited resources highlight the need for continued research into developing FCL. methods that can operate effectively under constrained conditions. By addressing the challenges of limited resources, we can pave the way for more practical and scalable applications of federated continual learning in real-world scenarios.
3.2.1 Ablation Study on Three Training Resources
We first explore the impact of each training resource on model performance and empirically select six baselines that may be sensitive to the corresponding resources and conduct experiments.
1. Does the Memory Buffer matter? As shown in Fig la we select six methods that need extra memory buffer to cache previous samples/synthetic samples/auxiliary datasets. Specifically, when allocated a modest amount of memory buffer, these methods experience a marked improvement in their performance. This enhancement becomes particularly pronounced for methods that leverage generative models and distilled data for datasets with simple features. However, as the complexity of the datasets increases, posing challenges such a wider diversity of classes and domains, the performance of these methods, particularly those reliant on generative modeling, undergoes noticeable decline. This underscores the limitations of solely relying on synthetic data generation or distillation in complex learning environments. In contrast, methods that employ straightforward sample caching exhíbit a more resilient performance profile. They maintain relatively stable performance, demonstrating their robustness against dataset complexity. Nevertheless, as the number of cached samples surpasses a certain threshold, the model's 's performance gradually approaches an upper limit, indicating diminishing returns from further sample accumulation. This plateauing effect underscores the need for a delicate balance in managing the memory buffer, aiming to maximize performance gains without incurring unnecessary computational cost.

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FedAug
FedCiL
Re-Fed
GLFC
Crea
AV-FCL
CIFAR10
CIPAR100
Tiny-lmagehet
Digit10
Offica33
Offke-Cattecl111
30
20
05
50
Test AcCuCcnn
24
10-
50
Memory Buffer
Memory Bufter
Hamorry Butfer
Hermory Butfee
Memory Buffer
Memory Butfer
(a) The impact of the Memory Buffer. We set four different memory buffers for each dataset (e.g.. M
{0, 300, 500, 1000} for both CIFAR-I0 and CIFAR-100), Here, we select six methods that use the memory buffer to cache samples from previous tasks, synthetic samples, or distillation samples.
FedAug
FedCil
FOT
GLFC
CFeO
AF-FCL
CIFAR10
CIFAR10O
Tiny-ImageNet
Digi:10
Office83
Ortke-Caterh-10
50
Test Accurscy
25
20
23
40
26
13
70
85
20
Computational Dudget
Compuutaiionll Budget
Computational Budget
Computational Budget
Computational Budget
Computatioal
(b) The impact of the Computational Budget. Here we choose the FCI. methods involved in complex computing. To normalize for effective computing due to the overhead of associated extra forward passes to decide on the distillation and training of the generative model, these additional calculations will share the computational budget equally with the target model.
Foddvg
FedCIL
Re
FCL Office3]
CIF1R10
CIFAR100
Tiny Immy-lmagefiet
Digit10
Office Calteh-C
Test Acturaty
03
20
45
22
15
15
65
10
20
10
20
70
100
20
30
70
200
10
33
150
20
30
70
100
15
20
120 10
25
102
Label Rate 196)
Label Rate (%)
Label Rate (76)
Label Rate (36)
Label Rate 1%6
Label Rate (%)
(c) The impact of the Label Rate. Here we equally test six random FCL methods with the ratio R
{ (10%, 30%, 70%, 100%) } for all datasets. Each FCL method trains the model just with the labeled data.
Figure 1: Ablation study on three training resources.
2. Does the Computational Budget matter? As depicted in Fig lb we carefully allocated varying computational budgets for each dataset, taking into account the maximum allowable limit specified in T It is evident from the results that when the computational budgets assigned to the various methods are restricted, the overall performance of the models inevitably suffers. This underlines the critical role that computational resources play in ensuring optimal model performance. However, certain methods demonstrate remarkable resilience, notably FedAvg and FOT. Even with limited computational budgets, they maintain satisfactory performance levels, highlighting their efficiency and effectiveness in resource-constrained environments. On the other hand, when the computational budget is abundant, allowing for unrestricted resource allocation, these other methods display the potential for higher performance ceilings. Given sufficient resources, they may achieve performance that surpasses that of FedAvg and FOT. Nevertheless, the practical implications of such high resource requirements must be carefully considered, as they muy not always be feusible or cost-effective in real-world applications.
3. Does the Label Rate matter? In Fig we analyze the relationship between the label rate and the model performance. Our analysis reveals a compelling trend: when the label rate falls below 70%, strategic increase in the proportion of labeled data within the dataset is potent catalyst for enhancing the performance of most model approaches. However, the Digit- 10 dataset stands out as a unique case. Despite boasting a vast quantity of data, the simplicity of its feature information, characterized by single-channel data, renders it less reliant on an extensive labeled dataset for optimal performance. Consequently, even with a relatively modest amount of labeled data, the model can achieve promising results, underscoring the importance of considering the dataset's s inherent characteristics when evaluating the label rate's impact. This observation underscores the nuanced interplay between the label rate, dataset complexity, and model performance. While
7

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CIFARO
CEARLCO
They Imagaiat
Dig
Office1
Office Cahacn-co
22
20
15
Rocd
8-10
Aveo
9:12
M--533
M-300C
Pe00
M4-500
N0-300 P-300 M-930
N--00C
M--00
M--13
M--30
P0-30
W5-75
R-004
Samote Cacking
Sampre Olicing
SanoteGeching
Somph Cocking
Sempl Gaching
Sampr Cocking
(a) Sample Caching. Here we select three FCL methods that require a cache of samples from previous tasks.
We designed three combinations of memory buffers and lahel rates to conduct the experiments.
20
M--500
M-330
Cate Synewss
Dara Syr
Deta Syttees
Des Sathers
Dee Syatess
Data Serthet
(b) Data Synthesis. Here we select three FCL methods that are involved in generative models. We designed
three combinations of memory buffers and computational budgets to conduct the experiments.
CIPR10
CrARico
Thy Imagaaat
office::
Ofc Ca
25
20-
15-
10
$1.00
$4:200
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(c) Knowledge Distillation. Here we select three FCL methods that employ knowledge distillation. We designed three combinations of memory buffers and computational budgets conduct the experiments.
Figure 2: Analysis of three typical FCL techniques.
higher label rate generally leads to improved performance, the extent of this improvement can vary significantly depending on the dutasel's 's charucteristics and the model being employed.
Conclusion. Existing methods have encountered limitations in achieving optimal results within resource-constrained envirommntss.. Notably, deploying a modestly sized memory buffer has emerged as a crucial factor in significantly augmenting model performance. This enhancement underscores the importance of an adequately provisioned yet economically efficient memory allocation strategy, as an overly generous buffer does not necessarily translate into commensurate gains in performance. similar trend is observed with the label rate, emphasizing the need for balanced approach in labeling data to maximize model efficacy. Moreover, the influence of the computational budget on FCL performance is intimately tied to the algorithmic design itself. Simple yet effective methods such as FedAvg demonstrate remarkable resilience, maintaining robust performance even with stringent computational constraints. This finding underscores the potential of streamlined FCL algorithms in addressing real-world challenges posed by limited resources. Experimental evidence highlights the imperative of selecting FCL methods tailored to the specific training resources. Researchers can achieve more effective learning outcomes by aligning algorithmic complexity with computational and memory resources.
3.2.2 Analysis of Typical FCL Techniques
In the experiments mentioned above, we have already observed that training resources play crucial role in the performance of cxisting FCL methods. Next, we will further explore how to consider resource constraints in the design of FCL algorithms. We will research the four techniques above, often used to alleviate catastrophic forgetting, separately to demonstrate the effectiveness of different FCL techniques to different training resources.
Does the Sample Caching matter? To gain a deeper understanding of the efficacy of sample caching technology within limited resources, we impose restrictions on the memory buffer size and the label rate illustrated in Fig 2a] Regarding the sample caching technique, the label rate is pivotal in determining the quantity and quality of samples available for local training. Specifically, higher label rate translates into more high-quality, labeled samples within the dataset, significantly enhancing the learning process. The Re-Fed algorithm, in particular, introduces innovative approach that
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leverages collaborative storage to intelligently select and retain the most critical samples when both the label rate and memory buffer are limited. This strategy particularly effective in boosting model performance by focusing on the most informative data points, even under stringent resource constraints. Furthermore, as the label rate and memory buffer size vary, they jointly influence the performance of the sample caching technique in complex ways. The label rate not only dictates the overall quality of the data but also serves as an upper bound on the number of samples that can be feasibly stored within the given memory buffer. Consequently, the effectiveness of the sample caching is intimately tied to the interplay between these two training resources. Optimizing the allocation and utilization of these resources is thus crucial for maximizing the benefits of sample caching with limited resources.
2. Does the Data Synthesis matter? Data synthesis techniques, which often harness the power of generative models, are instrumental in creating synthetic data for replay purposes in various applications. In FCL, where resource constraints are prevalent, the training of these generative models inevitably introduces additional computational overhead. Consequently, it becomes imperative to carefully manage not only the memory buffer but also the computational budget overhead to ensure the feasibility and efficiency of the data synthesis process. As depicted in Fig 26 the success of the data synthesis technique intricately linked to both the available computational budget and the memory buffer capacity. Initially, as the memory buffer size increases, it provides more room for storing and manipulating synthetic data, thereby enhancing the technique's ' effectiveness. However, after reaching a threshold where a sufficient quantity of data can be comfortably accommodated, the marginal benefit of further expanding the memory buffer gradually diminishes. At this point, the computational budget becomes the primary bottleneck as the increased computational demands associated with generating higher-fidelity synthetic data or processing larger datasets begin to outweigh the benefits gained from additional memory. This shift in the dominance of factors underscores the importance of striking a balance between memory and computational resources in the design and implementation of data synthesis techniques for FCI. Efficient allocation of these resources can help maximize the effectiveness of the technique while minimizing the overall overhead, enabling more robust and scalable FCL systems.
3. Does the Knowledge Distillation matter? Knowledge distillation is prevalent technique in mitigating catastrophic forgetting within existing FCL methods. While this approach effectively preserves key information across learning tasks, it necessitates the utilization of additional distillation datasets, whether publicly accessible or synthetically generated. Unfortunately, this requirement introduces additional complexity layers, including managing both computational overhead and memory buffer capacity. We primarily focus on limiting the computational budget, as it is often the primary factor constraining the scalability and speed of the distillation process. The Fig 2c illustrates that the computational budget is the primary limiting factor affecting the performance of the corresponding knowledge distillation methods. On the other hand, the memory buffer, though still a consideration, has a relatively minor impact on the overall performance of the knowledge distillation technique. This observation highlights the efficiency of the distillation process, where even a modest quantity of distilled data samples can achieve satisfactory performance gains. By efficiently managing the memory buffer to accommodate these necessary samples, we can maximize the benefits of knowledge distillation without incurring excessive memory overhead. In summary, our approach to knowledge distillation FCL involves a careful balancing act between computational budget and memory buffer, with greater emphasis on optimizing the former to ensure the technique's ss practicality and performance within the confines of federated and continual learning environments.
Discussion about the Network Extension. In this section, we do not perform additional experimental investigations specifically tailored for the Network Extension method. The main reason is that prevalent network extension methodologies typically presuppose knowledge of task boundaries, implying that the task ID for incoming data is accessible during inference. When this impractical assumption is relaxed, and we analogize to other methodologies that infer the category of the sample without prior task information, these extensions fail to uphold their erstwhile promising performance. as evidenced in Tables [ [
Conclusion. Three FCL strategies differ in strengths and limitations under varying resource con- ditions. With ample computational power and memory, sample caching preserves representative previous samples to combat forgetting, while data synthesis generates pseudo samples without storing real data- albeit requiring significant computational resources for effective rehearsal. Conversely,
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knowledge distillation demonstrates resource efficiency by maintaining performance with minimal distilled samples. bypassing large memory buffers. Resource availability thus dictates strategy se- lection: sample caching and data synthesis excel in resource-rich environments, while knowledge distillation becomes optimal under constraints. Future research should prioritize reducing synthesis computation, optimizing cached memory usage, and enhancing distillation efficiency to address growing demands for resource-constrained FCL implementations.
4 Evaluation & Future Research Direction
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Table 4: Performance comparison of various methods with a different order of domain tasks. We report the reverse order compared to the Table 2
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Dataset
Metric
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FedCIL.
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Figure 3: Performance w.r.t number of incremental tasks n for class-incremental datasets.
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In this section, we delve deeper into the analysis of existing FCL methodologies by conducting extensive quantitative experiments that focus two crucial aspects: the number of tasks within Class-IL datasets in Figure. 3 and the order of tasks across various Domain-IL, datasets in Table By systematically varying these parameters, we aim to gain a nuanced understanding of how these factors impact the performance of FCL methods. Firstly, we observe that as the number of tasks in the Class-IL datasets escalates, the performance of all the evaluated methods noticeably declines. Nevertheless, despite this performance decrement, the general trends exhibited by the different methods remain comparable, suggesting that the fundamental challenges they face are similar. Furthermore, our experiments reveal that the order in which domain tasks are presented to the model can significantly influence its ultimate performance within Domain-IL. scenarios. This finding emphasizes the importance of task scheduling and curriculum design in FCL systems, as certain sequences may be more conducive to learning than others. However, even with this variability in performance due to task order, each method maintains its distinctive strengths and tendencies, demonstrating its resilience and adaptability to different contexts.
Moreover, although there is lot of research on FCL, these studies mainly focus on transferring CL algorithms in centralized settings to alleviate catastrophic forgetting, ignoring the issue of training resource overhead. Based on this, we shed light on the future research direction in Appendix
Conclusion
This paper has presented a tutorial on FCL under resource constraints. Firstly, we begin with an introduction to the existing FCL methods and three training resources on distributed devices that influence the performance of the methods. Then, we conduct experiments using ablation studies on each training resource and analyze the effect of four typical FCL techniques on alleviating catastrophic forgetting in FCL. Finally, we discuss future research directions in resource-constrainee FCL.
Acknowledgments and Disclosure of Funding
This work is supported by the National Key Research and Development Program of China under grant R024YFC3307900; the National Natural Science Foundation of China under grants 623761 03, 62302184, 62436003 and 622061022 Major Science and Technology Project of Hubei Province under grant 2024BAA008; Hubei Science and Technology Talent Service Project under grant 2024DIC078; Ant Group through CCF-Ant Research Fund; and Fundamental Research Funds for the Central
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Universities under grant YCIJ20252319. The computation is completed in the HPC Platform of Huazhong University of Science and Technology.

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A Background and Related Work
Federated Learning. Federated Learning (FL) is an approach to developing unified global model by combining models trained on local, private datasets from multiple clients [31 32 56, 39]. A notable FL framework. FedAvg [38], enhances the global model by averaging the parameters of these locally trained models. Nevertheless, conventional FL. methods, such as FedAvg, encounter difficulties when dealing with data heterogeneity, where client datasets are not independently and identically distributed (Non-IID), leading to reduced model performance [3 12 46]. To address the Non-IID problem in FL, an optimization technique incorporating a proximal term was introduced in [22] to alleviate the impact of diverse and Non-IID data distributions among devices. Another strategy, federated distillation [67] 72). focuses on transferring knowledge from multiple local models to the global model by aggregating solely the soft predictions produced by each model. The authors of 33 presented knowledge distillation technique that employs unlabeled training data surrogate dataset. However, these methods are tailored to address static data with spatial heterogeneity and do not account for the challenges presented by temporally heterogeneous streaming tasks in FL.
Continual Learning Continual Learning (CI.) is machine learning paradigm that enables models to learn sequentially from a stream of tasks while preserving knowledge acquired from earlier tasks CL methods can be broadly categorized into three primary approaches: replay-based 47 regularization-basee [16 [7). and parameter isolation techniques [52 43 replay-based
51 73 70).
methods involve retaining a subset of previous task samples to maintain knowledge when learning new tasks. Regularization-base methods prevent the overwriting of existing knowledge by applying constraints to the loss function during the learning of new tasks. Parameter isolation methods usually introduce extra parameters and computational resources to facilitate the learning of new tasks. In this work, we concentrate on federated continual learning, which integrates elements of both federated and continual learning.
Federated Continual Learning Federated Continual Learning (FCL) is introduced to tackle the challenge of learning from sequence of tasks on each client, focusing adapting the global model to new data while preserving knowledge from previous data (57 53 63). Although important, FCL has only recently started to receive attention, with [62] serving as seminal work in this area. This study concentrates on Task-Incremente Learning (Task-IL), which necessitates distinct task IDs at inference time and employs dedicated task-specific masks to improve personalized performance. Concurrently, research efforts like [64] consider the multi-granulariin representation of knowledge, fostering the integration of spatial- temporal knowledge in Federated Continual Incremental Learning (FCIL). The authors of 30) align local domain knowledge with a dynamic network to strike a balance between resource usage and model performance. Other works, such [77, employ proxy dataset and leverage knowledge distillation at both the server and client levels. In contrast, (5] simplifies the problem by assuming clients have sufficient storage store and share old examples, deviating from the conventional FL framework. Additionally, studies like [6] [5 investigate FCL in applications beyond image classification.
B Problem Formulation
Federated Learning. In the context of Federated Learning (FL), a common problem involves collaboratively training a global model across K clients. Each client & has exclusive access to its local private dataset, denoted as To {xr(²) (1)
0k (8) where 2:(0) represents the i-th data sample, and y E {1, (........ C} is the associated label for C' classes. The size of dataset Ty is indicated by [Tel The global dataset is the aggregate of all local datasets, i.e., (Ti .2...., Tk ). with
T T= Tx- The goal of the FL system is to train a global model w that minimizes the overall empirical loss across the entire dataset D:
K
Thl Cx(w),
min L(w)= =
where L*(w)
Lo v/
(I)
TT
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Table 5: Experimental Details. experiments section.
Analysis of various considered settings of different datasets in the
Attributes
CIFAR-10
CIFAR-100
Tiny-ImageNet
Digit-10
Office-31
Offic--Caltech-10
Task size
178MB
178MB
435MB
480M
88M
58M
Image number
60K
60K
120K
110K
4.6k
2.5k
Image Size
3x32x32
3x32x32
3x64x64
1x28x28
3x300x300
xx300x300
number
Task Scenario
Class-LL
Class-LL
Class-LL
Domain-IL
Domain-IL
Domain-11
Batch Size
=
#= 64
=
== 64
== 32
ACC metrics
Top-1
Top-1
Top-10
Top-1
Top-1
Topp1
Learning Rate
0.01
0.01
0.001
0.001
[ 0.01
0.01
beterogeneity
a
0 0.1
o= 1.0
Client numbers
C = 20
C=20
C=20
C=15
C=10
C=8
Local Iraining epoch
E = 20
=
E 20
=
=
=
Client selection ratio
è 0.4
k =.4
=
k 0.4
=.4
0.5
Communication
Memory Bufler
M = 1000
M 1000
2000
M 1500
M =
=00
Lahel Rate
Rt 100%
ft 100%
R= 100%
It 100%
R= 100%
R= 100%
Computational Budget
== 1000
=00
B = 2000
B 1700
B 200
B 150
Here La (w) signifies the local loss for client k, and Lex is the cross-entropy loss function, which quantifies the discrepancy between the predicted and actual labels.
Continual Learning. In a typical Continual Learning (CL) scenario (outside of federated context), model is trained on sequence of tasks {T 1 ,,...., ]" where T represents the t-th task. Each task T === -i
i= (ar (i) comprises N pairs of data samples xz! E 22 and their corresponding labels ( E y The domain space and label space for the t-th task are denoted by X' and y", respectively, with [)* classes. The total class across all tasks is y U²=1 ) and similarly, the total domain space is X' = U X This paper focuses two CL scenarios: (I) Class- Inccemnnnrllttaal Task: All tasks share the same domain space (X' = X for all t E [n]), but the number of classes may vary ()21 * yd for all E (m)). (2) Domainn-Incremene
Task: All tasks have the same number of classes () yr for all & E (n)), but the domain and data distribution change over tasks (X of 2 for all E (n))).
Federated Continual Learning. We combine the CL with the federated setting. Our objective is to train a global model for K clients, where each client & can only access its local sequence of tasks (Tx T Tx) J. When the t-th task arrives, the goal is to train a global model w across all t tasks, denoted by T 1E kk=1 Th"). This be formulated as:
K N ΣΣΣ
n = arg min
( Ler(w: x Fn (2))
WERd
T
n=1
Here LCE is the cross-entropy loss function used to measure the discrepancy between predictions and true labels.
B.1
Experiment Setup
B.1.1 Datasets.
Our experiments utilize diverse datasets distributed across two federated incremental scenarios, encompassing six datasets in total.
This dataset gradually introduces classes. It begins with subset of classes and expands by adding more classes in subsequent stages, facilitating models to learn and accommodate increasing range of classes.
Gass-Incremental Task Dataset:
(1) CIFAR- 10 [78): Comprises object classes, encompassing everyday items, animals, and vehicles. It includes 50,000 training and 10,000 test images.

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(2) CIFAR- 100 [18] Similar to CIFAR-10 but with 100 detailed object classes. It contains 50,000 training and 10,000 test images.
(3) Neny-mmageNet 120): A selection from the ImageNet dataset, featuring 200 object classes. includes 100,000 training images, 10,000 validation images, and 10,000 test images.
Domain-Incrrmmntte Task Dataset: This dataset progressively introduces new domains. starts with samples from a specific domain and incorporates additional domains in later stages, allowing models to generalize to new contexts.
(I) Digit-10: The Digit- 10 dataset includes 10 digit categories across four domains: MNIST (1]
EMNIST B1, USPS 14, and SVHN [40]. Each dataset represents specific domain, such as handwriting style, and contains 10 digit classes.
MNIST: Contains 60,000 training and 10,000 test handwritten digit images.
EMNIST: An extension of MNIST with 240,000 training and 40,000 test handwritten character (letter and digit) images.
USPS: Includes 7,291 training and 2,007 test handwritten digit images from the US Postal Service
Features 73,257 training and 26,032 test images of house numbers captured from Google Street View.
SVHN:
(2) Office-31 50:: Contains images from three domains: Amazon, Wehcam, and DSLR. covers 31 categories, with approximately 4,100 images per domain.
(3) Office-Caltech- 10 169:: Includes images from four domains: Amazon, Caltech, Webcam, and DSLR. It comprises 10 object categories, with around 2,500 images per domain.
B.1.2 Baselines.
In this paper, we follow the same protocols proposed by 38 47 to set up tasks. We evaluate our resource-constrained settings with the following baselines. FedAvg [38]: It is representative FL model that aggregates client parameters in each communication round. It simple but effective model for FL.
FedProx 122 It is also a representative FL model, which better at handling heterogeneity in federated networks than FedAvg.
FedAvg/FedProx-ER [49] Experience Replay is a technique primarily utilized in reinforcement learning, especially in deep reinforcement learning algorithms. Here, we combine the ER algorithm with the FL. algorithms FedAvg and FedProx.
This method addresses the federated class-incrementàà learning issue and trains a global model with additional class-imbalance losses. proxy server used reconstruct samples help select the best old models for model updates.
GLFC [5]
This method employs the ACGAN network to generate synthetic samples to consolidate the global model and align sample features in the output layer. The authors conducted experiments in the FCIL scenario, and adopted it in our FDIL setting.
FedCIL 45
TARGET 66): This method uses a generator to synthesize data to simulate the global data distribution on each client without additional data from previous tasks. The data heterogeneity is considered with catastrophic forgetting, and the authors focus on the FCII. scenario.
AF-FCL [58]: This method focuses on the data noise among previous tasks, which degrades the model performance. The NF model is used quantify the credibility of previous knowledge and select the transfer knowledge.
Re-Fed 23 This approach proposes synergistic replay for each client selectively cache samples based on the understanding of both local and global data distributions. Each client trains an additional personalized model discern the importance of the to each client.
This method uses orthogonal projection technology to project different parameters into different spaces to isolate task parameters. In addition, this method proposes secure parameter
FOT 2
25

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aggregation method based on projection. In the model inference stage, the method requires assuming that the boundaries of the task are known. We modified an automated inference of the model.
CFeD [3 It employs public datasets as proxy data for knowledge distillation at both client and server sides. Upon introducing new tasks, clients use proxy data transfer knowledge from previous to current models thus mitigating inter-task forgetting.
Fed 62]
This method divides parameters into task-specific and shared parameters. A multi- head model based on known task boundaries is used in the original method. To ensure a fair comparison, we modify it to automate the inference of the model.
C Future Research in Resoarce-Constrained Federated Continual Learning
In this paper, based on extensive experiments, we have proved that training resources play a crucial role in the effectiveness of existing FCL methods. Therefore, we propose the following suggestions for future research:
Sampling Strategy: Sample caching is mainstream method prevent catastrophic forget- ting in FCL, and it often has good results when memory buffer resources are not tight. In our experiments, we used simple strategies such as Random, FIFO, and LIFO. However, apart from the collaborative replay strategy proposed in (33]. there is currently no in-depth exploration of sample caching strategies FCL. In addition, future research can also focus directly selecting exemplar samples during local training to train and cache locally.
Update Optimization: Common SGD optmizatiinn often achieves expected performance under IID data, but in FCL, data exhibits heterogeneity in both spatial and temporal dimen- sions. Better optimization approaches can be considered, such as using analytic learning estimate parameters and employing sharpness-awe aware minimization to replace empirical risk minimization), ultimately enhancing the model's 's robustness to data heterogeneity and improving model performance.
Bandwidth Restriction: Bandwidth plays a crucial role in distributed systems, the commu- nication frequency between different clients and servers directly impacts the convergence of the model. In FCL, due to the continuous arrival of incremental tasks, clients need to participate in federated training for extended periods, resulting in substantial communication overhead. Researching how reduce this communication overhead of great significance for the large-scale deployment of FCL.
Adaptive Allocation: Due to limited training resources, optimizing their allocation to boost model performance represents pragmatic approach. This could entail allocating appropriate memory buffers to different datasets and assigning computational resources to particular processes. One viable method is the application of combinatorial optimization techniques. In practical implementations or method designs, dynamically allocating resources to each technique based on availability can further improve performance.
26


Reviews:
[{"review_id": "rKOLUlEuCA", "reviewer": "Reviewer_WvFo", "summary": "The paper presents a large-scale empirical evaluation of twelve federated continual learning (FCL) methods under three practical resource constraints: (1) memory buffer budget, (2) computational budget, and (3) label rate (i.e., the fraction of labeled samples available during training). The evaluated methods span four major categories: replay-based methods, regularization-based methods, knowledge distillation, and network extension. The results show that no single FCL method is universally optimal across all resource-constrained settings.", "questions": "1. Several non-federated CL works have studied resource constraints extensively. How does this paper build on or differ from them? What challenges are specific to the federated setting that make these constraints harder to handle?\n2. Why is communication overhead not considered as part of the resource constraints? Since communication is a major bottleneck in real-world FL systems, an analysis or at least a discussion would improve the paper’s applicability and uniqueness.\n3. Could the authors more precisely formalize how each resource constraint is defined and enforced in the experiments? For example, how is computational budget quantified (e.g., gradient steps vs. training time), and how is label rate sampled and applied across clients and tasks?", "limitations": "no", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 1, "originality": 1, "strengths_and_weaknesses": "***Strengths***\n* **[Large-scale (in terms of GPU hours) empirical evaluation]**\nThe paper conducts a large-scale empirical study of federated continual learning methods under three key resource constraints: memory buffer size, computational budget, and label rate. The evaluation spans twelve representative baselines across multiple FCL techniques and datasets, with over 1,000 GPU hours of experiments. \n* **[Clarity in organizing techniques and findings]**\nThe paper organizes FCL methods into four technique categories (sample caching, data synthesis, knowledge distillation, and network extension) and analyzes their performance across varying levels of resource availability. This structured approach makes the findings easier to follow.\n\n***Weaknesses***\n* **[Prior work and citation depth]** The paper in its current form lacks discussion of what has been done in the literature. For example, several studies have proposed ways to isolate computational budget and investigate the performance of CL methods when the computational budget is normalized [41, A]. While the paper cites [41], it does not mention how that work evaluates methods under this constraint, nor does it build on any of its insights. Similarly, several works have studied continual learning from partially labeled streams [B, C, D, E], but these are not discussed at all. I understand that these are not federated continual learning papers, but they are still CL papers and highly relevant. Finally, the label rate constraint has already been studied even in a federated continual learning setup, specifically in a medical imaging context [F].\n* **[Missing federated-specific challenges]**\nRelated to the previous point, since all three constraints have been well studied in the CL literature, I expected this paper to focus more on complications that arise specifically from the federated aspect. For example, why is there no discussion of federated communication costs? The paper would benefit from clarifying the specific federated scenario it addresses and clearly delineating how it differs from standard CL, before formalizing the particular resource constraints under study.\n* **[Dataset realism and scalability]**\nThe datasets used are mostly small-scale, and it is unclear whether the findings would generalize to a real-world FCL scenario, which the paper seems to aim to simulate.\n\n[40] Prabhu, Ameya, et al. \"Computationally budgeted continual learning: What does matter?.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n[A] Ghunaim, Yasir, et al. \"Real-time evaluation in online continual learning: A new hope.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[B] Gomez-Villa, Alex, et al. \"Continually learning self-supervised representations with projected functional regularization.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n\n[C] Zhang, Wenxuan, et al. \"Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation.\" The Twelfth International Conference on Learning Representations.\n\n[D] Csaba, Botos, et al. \"Label delay in online continual learning.\" Advances in Neural Information Processing Systems 37 (2024): 119976-120012.\n\n[E] Caccia, Lucas, and Joelle Pineau. \"Special: Self-supervised pretraining for continual learning.\" International Workshop on Continual Semi-Supervised Learning. Cham: Springer International Publishing, 2021.\n\n[F] Alhamoud, Kumail, et al. \"Fedmedicl: Towards holistic evaluation of distribution shifts in federated medical imaging.\" International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024.", "was_revised": true, "final_justification": "I would like to thank the authors for their detailed rebuttal, and I apologize for not engaging more during the discussion phase due to an extenuating personal circumstance that required my urgent attention. I have read the rebuttal carefully, along with the other reviewers’ comments and the available discussion. I also had some internal discussion with SBXG, which was useful.\n\nI think the rebuttal and SBXG’s input have addressed my major concerns. However, I still find the contribution limited, since it does not include any specific experiments that show trends that are true in FCL under resource constraints but do not appear in centralized CL under resource constraints. A future paper that does this would be a lot more impactful.\n\nThat said, the example of whether insufficient buffer size on clients leads to replay distribution drift that ultimately undermines global knowledge consistency is a good way to distinguish FCL from centralized CL, and I strongly encourage the authors to include it in the paper. I also encourage them to incorporate the clear definitions of resource constraints provided in the rebuttal.\n\nI understand that they initially missed the MICCAI paper, as the field is growing very rapidly, and I hope the references I provided help position the work so that readers can systematically build on relevant CL and FCL results in a systematic way. Finally, I think the future inclusion of ImageNet and DomainNet would make whatever code base published by the authors a lot more attractive.", "rebuttal": "> **W1&Q1.  Unique Nature of Resource Constraints in Federated Continual Learning.**\n\n**R1:** Thank you for the insightful comment. We fully agree that resource constraints have been extensively explored in centralized continual learning (CL). Our work is motivated by these studies, and we would like to clarify the key distinctions and contributions of our work in the context of Federated Continual Learning (FCL):\n\nFirst, in terms of research focus, prior works often examine the impact of a **single** constraint (e.g., computation budget or labeling rate). In contrast, this paper is, to the best of our knowledge, the first to **systematically investigate the joint effects** of memory buffer size, computation budget, and labeling cost in FCL. We conduct comprehensive evaluations across representative techniques, including generative replay, knowledge distillation, sample-based replay, and regularization-based methods, totaling over 1000 GPU hours.\n\nSecond, FCL differs fundamentally from traditional CL in terms of scenario characteristics and research objectives. FCL involves distributed collaborative training among multiple clients with heterogeneous data distributions (simulated via Dirichlet partitions), where privacy constraints prohibit sharing of raw data. Clients must make local decisions based only on limited feedback. Therefore, unlike centralized CL that focuses on optimizing a single model, our goal is to examine **how resource constraints disrupt the collaborative mechanisms unique to FCL**—for example, whether insufficient buffer size on clients leads to replay distribution drift that ultimately undermines global knowledge consistency.\n\nFinally, we thank the reviewer for pointing out [F], which studies label-efficiency in a FCL.  **MICCAI is a top-tier conference in the medical domain, and related methods primarily emphasize applications to medical tasks, whereas our work focuses on more general algorithmic research from the ML/CV/AI community.** Due to this difference in focus, we were previously unaware of [F]. This is highly relevant, and we will cite and discuss it properly.\n\n> **W2&Q2.  Exploration of Communication Cost.**\n\n**R2:** Thank you for your insightful comments. We fully agree that communication overhead is one of the core challenges in FL, especially in real-world deployments. In this work, we did not explicitly include communication cost as one of the three core resource constraints, mainly due to the following considerations:\n\n**Communication cost is fundamentally different from the client-side training constraints we focus on.** Our study primarily targets three types of **client-local resource limitations**: memory buffer, computational budget, and label rate. These are hard constraints that directly affect the training capacity of edge devices. In contrast, communication cost is mainly determined by factors such as communication frequency, model synchronization strategy (e.g., number of rounds $T$), and model size. **Therefore, it impacts training latency rather than the local training capability of clients.**\n\n**Communication constraints deserve dedicated treatment in future studies.** We appreciate your point that communication cost is an important dimension in FCL. For example, future studies could explore communication heterogeneity (e.g., bandwidth and latency across clients), the applicability of compression/sparsification techniques in FCL, or trade-offs between communication and local computation. As noted in Appendix E, we have already listed communication constraints as a direction for future work. In the current paper, we aim to first provide a systematic understanding of client-side resource limitations, which we believe is a necessary step toward building a more comprehensive performance analysis framework for FCL.\n\n> **W3. Concerns about Datasets.**\n\n**R3:** Thank you for raising the concern regarding dataset scale. We fully understand and agree that large-scale datasets are important for simulating realistic FCL scenarios. We address your comments as follows:\n\n**1. The selected datasets are common datasets in FCL research, ensuring representativeness and fairness**\n\nIn this work, we evaluate our method on six publicly available datasets (CIFAR-10/100, Tiny-ImageNet, Digit-10, Office-31, and Office-Caltech-10), which are widely adopted in existing FCL literature to assess algorithmic performance under class-incremental (Class-IL) and domain-incremental (Domain-IL) settings. These datasets offer the following advantages:\n\n- They cover **diverse application scenarios**, including natural images (CIFAR, Tiny-ImageNet), handwritten digits (Digit-10), and cross-domain settings (Office series);\n- They span **a range of class complexities**, from 10 to 200 classes, enabling meaningful evaluation of interference and forgetting during continual learning;\n- They are **easy to reproduce and widely used**, facilitating fair and standardized benchmarking.\n\n**2. We plan to extend the evaluation to larger FCL datasets such as ImageNet and DomainNet**\n\nWe greatly appreciate your suggestion to further validate the scalability of our method. We plan to expand our experiments to large-scale datasets like **ImageNet** and **DomainNet** in future work. Due to the significant training cost, we are currently unable to provide full-scale results during the rebuttal phase, but we will prioritize this direction in our subsequent research.\n\n\n> **Q3. Clear Definition of Resource Constraint.**\n\n**A3:** Thank you for your question regarding the precise definition and application of resource constraints. Below, we formalize the three key constraints considered in our work — memory buffer (M), computational budget (B), and label rate (R) — and explain how they are consistently enforced in our federated continual learning (FCL) experiments.\n\n**1. Memory Buffer (M)**\n\n- **Definition**:\n  $M \\in \\mathbb{N}$ represents the maximum number of samples that a client can store from **previous tasks**. This buffer may include:\n\n  - real samples from earlier tasks,\n  - synthetic samples generated by a local generative model,\n  - or samples used for distillation-based replay.\n\n- **Enforcement**:\n  At task $t$ , each client builds its local training set as:\n  $$\n  D_\\text{train}^{(t)} = D_\\text{current}^{(t)} \\cup D_\\text{buffer}^{(t-1)}, \\quad \\text{with} \\quad |D_\\text{buffer}^{(t-1)}| \\leq M\n  $$\n  When the cache data exceeds $M$, we retain $M$ samples through random sampling.\n\n  This ensures that the memory usage for prior knowledge remains strictly bounded.\n\n**2. Computational Budget (B)**\n\n- **Definition**:\n  $B \\in \\mathbb{N}$ denotes the maximum number of **gradient update steps (i.e., backward passes)** a client may perform **during local training in total  communication rounds**. This constraint is **applied only on the client side**, since server-side resources are typically not the bottleneck in real-world FCL systems.\n\n  This budget includes **all client-side components**, such as:\n\n  - the main local model,\n  - local generative models,\n  - local personalized models,\n  - and distillation losses.\n\n- **Enforcement**:\n  In PyTorch, we track the number of times the `.backward()` function is called during local training. Once a client reaches or exceeds $B$ calls to `backward()`, its local training is immediately terminated:\n\n  ```python\n  if backward_calls >= B:\n      break  # Stop training for this client\n  ```\n\n  This unified constraint ensures fair and consistent computational load across different methods.\n\n**3. Label Rate (R)**\n\n- **Definition**:\n  $R \\in (0, 1]$ defines the **fraction of labeled data** available to each client per task.\n\n- **Enforcement**:\n  For a given task $t$, each client samples a labeled subset from its local data:\n  $$\n  D_t = D_t^{\\text{labeled}} \\cup D_t^{\\text{unlabeled}}, \\quad \\text{with} \\quad |D_t^{\\text{labeled}}| = R \\cdot |D_t|\n  $$\n  Only $D_t^{\\text{labeled}}$  is used for loss computation."}, {"review_id": "E265aHvzzN", "reviewer": "Reviewer_ef6U", "summary": "This paper presents an extensive empirical investigation into Federated Continual Learning (FCL) methods under practical resource limitations, focusing on memory constraints, computational budgets, and varying label availability. The authors evaluate over 10 FCL algorithms across six datasets, examining both Class-Incremental and Domain-Incremental learning scenarios. Their rigorous experimentation (exceeding 1,000 GPU hours) reveals a concerning performance gap: most current state-of-the-art FCL approaches significantly degrade when operating within realistic resource boundaries. The work systematically analyzes four fundamental FCL techniques—sample caching, data synthesis, knowledge distillation, and network extension—providing valuable insights into their relative effectiveness under various constraint profiles.", "questions": "1）：Based on the existing experimental findings and insights, developing a novel method that effectively performs FCL under resource-constrained conditions would significantly enhance the paper's contribution to the community. The current analysis provides valuable empirical observations that could serve as a foundation for methodological innovation.\n\n2）：The experimental setup presents idealized resource constraints that may not fully reflect real-world deployment challenges. Further analyzing memory and computation in terms of MB and FLOPs would provide more practical insights when deploying to mobile devices. Additionally, resources in real-world scenarios often fluctuate dynamically - a critical aspect overlooked by the current experimental framework. The paper should explore whether similar insights hold under dynamically changing resource conditions to better reflect practical applications.\n\n3）：Regarding dataset selection, testing on a broader range of datasets would better evaluate the effectiveness of these methods while validating the robustness of the insights. The current focus on CIFAR and its variants may not capture the complexity and diversity of real-world data. Expanding experiments to include more challenging datasets such as text, speech, and multimodal data would strengthen the paper's claims and increase its practical relevance.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strength**\n\n1）：The paper addresses a critical yet underexplored gap—real-world deployment feasibility of FCL systems under tight resource budgets (memory buffer, computational budget, and label rate data), which is vital for edge computing and mobile devices.\n\n2）： Extensive experiments (totaling over 1,000 GPU hours) across six datasets and more than ten baseline methods evaluate the performance of various approaches under different resource-constrained settings.\n\n3）：This paper provides valuable insights to guide future FCL method design. The three FCL strategies exhibit distinct strengths and limitations under varying resource conditions. For instance, knowledge distillation demonstrates remarkable resource efficiency by maintaining performance with minimal distilled samples, effectively circumventing the need for large memory buffers.\n\n\n\n**Weaknesses**：\n\n1）：The paper primarily analyzes existing methods without proposing a novel approach to address the identified problems, limiting its contribution to empirical insights. This lack of methodological innovation diminishes its novelty and contribution.\n\n2）：While the paper examines method performance under various memory and computational constraints, it uses less practical metrics. In real-world applications, memory should be measured in MB or GB, and computation more universally expressed as FLOPs, rather than the paper's approach where \"memory means the number of cached samples in edge storage, and computational budget denotes the gradient steps during model update.\" MB-based memory metrics and FLOPs would provide more meaningful practical insights.\n\n3）：The experimental evaluation primarily focuses on relatively simple datasets such as CIFAR and its variants. Real-world scenarios typically involve more complex and diverse data, including text, speech, and multimodal data. Therefore, the experimental section lacks comprehensive evaluation on more challenging and diverse data types that would better reflect practical applications.", "was_revised": true, "final_justification": "I still think this paper is not good enough to be accepted.", "rebuttal": "> **Q1. Concerns about Novelty**\n\n**R1:** Thank you for your thoughtful comments and suggestions. We fully understand and appreciate the expectation for methodological novelty, and would like to clarify the positioning of our work.\n\nThe primary goal of this paper is not to propose a new algorithm, but rather to **conduct the first systematic study of Federated Continual Learning (FCL) under resource-constrained conditions**. While many FCL algorithms have been proposed, there is still a **lack of a unified and standardized evaluation framework** to assess their robustness and generalizability when subject to client-side resource limitations (e.g., memory, computation, and label availability). To address this gap, we establish a large-scale benchmark that models three core resources (Memory/Computation/Label), spans six datasets, includes two task types (Class-IL/Domain-IL), and compares over ten representative methods.\n\nAs you rightly pointed out, “such analysis could serve as a foundation for future method development,” and we fully agree. We sincerely appreciate your suggestion to extend this work toward method innovation. In fact, our team has already begun designing **new strategies for resource-constrained FCL** for technical research paper, such as adaptive resource allocation policies that dynamically optimize memory and computation through combinatorial optimization—directly inspired by the empirical insights from this study.\n\nOur current priority is to ensure the analysis is **comprehensive, systematic, and reproducible**, with the hope of benefiting the broader community. In future work, we will build on this benchmark to develop **resource-aware FCL methods**, for which the empirical findings of this study provide a necessary and solid foundation.\n\n> **Q2. (a) Suggest using MB and FLOPs to analyze memory and computation more precisely. (b) The paper should discuss whether similar findings hold under dynamically changing resource constraints.**\n\n**R2:** Thank you for your insightful suggestions. We address your concerns point by point:\n\n(a) We chose to quantify memory (M) by number of samples and computation (B) by number of local gradient steps primarily for the sake of  **fair comparisons** across different baselines in FCL:\n\n- **Memory:** Different datasets vary significantly in sample size. For example, CIFAR-10 samples are 32×32 pixels, while Office-31 includes samples with resolutions up to 4288×2848. Using a fixed unit like megabytes (MB) would result in **inconsistent memory constraints across datasets**, making fair comparison difficult. In contrast, using the **number of samples** directly reflects the **effective memory capacity** for techniques like replay or generation and maintains alignment with their operational semantics.\n- **Computation:** FLOPs depend heavily on model architectures. In our study, some baselines adopt different backbones due to method-specific designs (e.g., FedCIL uses ACGAN, FOT uses AlexNet; see Appendix C). Thus, **FLOPs are not architecture-agnostic** and would introduce bias. We adopt **number of local gradient steps** as a model-independent metric that reflects **actual local computation**: more steps require more local resources regardless of the architecture. Additionally, FLOPs measure **theoretical computation cost** only, while **real-world efficiency** is affected by GPU parallelism, memory bandwidth, data I/O, etc., making FLOPs an **unstable or indirect indicator** of computational burden.\n\n(b) We fully agree that resource constraints in real-world FCL scenarios are often dynamic. For instance, edge devices like smartphones may experience fluctuating compute and memory availability; communication conditions can vary over time, affecting synchronization; labeling frequency and quality may also shift depending on data acquisition or user availability.\n\nOur current study adopts **static resource constraints** with the following goals:\n\n- To **control experimental variables** for a clean analysis of robustness and adaptability under constrained resources;\n- To **establish a standardized resource modeling protocol** as a foundation for future method development;\n- To **quantify the sensitivity and degradation trends** of various algorithms under comparable resource settings.\n\nWhile we do not simulate dynamic conditions explicitly, some **empirical patterns may indirectly reflect method adaptability**. For example, FedAvg and FOT demonstrate relatively stable performance under tight constraints, suggesting potential suitability for variable environments. Re-Fed’s collaborative replay mechanism can be extended to **dynamically prioritize cached samples** in response to resource fluctuations.\n\nIn summary, our key findings—that **the match between resource types and method properties governs performance**—are expected to hold under dynamic conditions, with the dynamics mainly affecting the **amplitude** rather than the **direction** of performance change. We sincerely appreciate your suggestion and will incorporate this important perspective into future work to enhance the practical relevance of our study.\n\n> **Q3. Concerns about Real-world and Multi-Modal Datasets.**\n\n**R3:** We thank the reviewer for the helpful suggestion regarding dataset diversity. We fully agree that a broader range of datasets is crucial for validating the robustness of our conclusions. In this study, we have already included several datasets beyond the CIFAR family to better reflect realistic scenarios:\n\n- **Digit-10** covers handwritten digit images from multiple domains;\n- **Tiny-ImageNet** introduces higher task complexity with 200 diverse categories;\n- **Office-31** and **Office-Caltech-10** contain real-world object images collected across different devices and environments, offering greater heterogeneity in data distribution and domain shift.\n\nThese datasets were carefully chosen to ensure diversity in **task complexity** and **data distribution**, thereby enhancing the practical relevance of our benchmark.\n\nRegarding non-vision modalities such as **text** or **speech**, we agree that they represent valuable directions for extending the practical applicability of FCL. However, the primary goal of this work is to establish a **benchmark framework for analyzing FCL under resource constraints**, and most existing FCL methods are still primarily validated on **image-based datasets**. We believe that our current setup sufficiently supports the core conclusions and serves as a solid foundation for future extensions to non-vision domains."}, {"review_id": "JDw5bGsxA3", "reviewer": "Reviewer_duEh", "summary": "The paper benchmarks state-of-the-art approaches applied to Continual Learning in the context of federated learning in resource-constrained settings. From their experiments, they conclude that all existing Federated Continual Learning approaches fail to achieve expected performance in resource-constrained settings - Memory Buffer, Computational Budget, and Label Rate.", "questions": "The one key contribution stated by the authors is the future research directions based on their benchmarking experiments, which the authors ask us to read from Appendix E. To be fair, we can evaluate the merits of the paper only by looking at the main contents of the paper. The authors should consider reorganizing the contents in a way that key contributions and takeaways stay in the main pages of the paper, and only supplementary information should be moved into the Appendix.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Overall, it is an interesting research problem to understand the impact of resource constraints in Federated Continual Learning.\n\nHowever, the paper has some weaknesses that stem from the paper's organization and writing clarity, which hinders its effectiveness.\n\n1. Lack of clarity in experimental takeaways- key takeaways from experiments (3.2) before the ablation study are not clear. Since this is a benchmarking paper, it is really important to clearly highlight the experimental takeaway and conclusions, and subsequent discussions. \n\n2. Insufficient explanation of key concepts -  For Example, Section 3.1.1. It would be good to explain in one or two sentences what Class Incremental learning and Domain Incremental Learning are. The paper should be self-contained, rather than always asking the readers to refer to the Appendix. It's important to explain these concepts clearly as they set the premise of the research problem discussed in the paper.\n\n3. Missing citations in introduction from lines 55 to 85; it's important to cite when you mention other works. For example, in lines 55 and 56, for many existing methods, particularly those based on replay [cite], additionally, some other methods relying on regularization [cite]\n\n4. Authors should stay within the allowed page limit to explain their work. The appendix should be used only for supplementary information, not for discussing any of the key contributions of the paper.", "was_revised": true, "final_justification": "The response from the authors is convincing and satisfactory.", "rebuttal": "> **W1. Key takeaways from experiments (3.2) before the ablation study are not clear.**\n\n**R1:** We sincerely thank the reviewer for the insightful feedback. Below, we clarify the design rationale of Section 3.2 and summarize the key findings to address your concerns.\n\nThe structure of **Section 3.2** is intended to reflect a progressive analysis — from holistic observation to root cause attribution. **We first establish baseline comparisons by contrasting “resource-rich” and “resource-constrained” settings**, allowing us to assess the inherent strengths and weaknesses of various method families (e.g., generative replay, sample-based replay, knowledge distillation). In resource-rich scenarios, we observe, for example, the limitations of generative models on complex datasets, which serves as a reference point for later analyses under constrained conditions.\n\nSubsequently, through a structured ablation study, we control combinations of memory buffer (M), computational budget (B), and label rate (R), aiming to **isolate and analyze the independent and joint effects of different resource dimensions on performance**. This design allows us to identify specific bottlenecks for different methods — for instance, the strong dependency of generative models on compute, or the sensitivity of replay-based methods to memory buffer size — providing empirical evidence to support the core conclusions.\n\nTo better emphasize the role of ablation studies in Section 3.2, we will revise the main text to explicitly highlight the following findings:\n\n- **Method performance is strongly tied to dataset complexity.** Generative replay methods perform well on simple datasets but degrade significantly on more complex ones, revealing their limited adaptability to high-diversity data.\n- **Auxiliary components (e.g., generators, distillation modules) amplify the impact of resource constraints.** When all three critical resources (memory, computation, labeling) are simultaneously constrained, all methods experience significant performance drops. In contrast, base methods that do not rely on additional modules (e.g., FedAvg + FedProx) demonstrate the most robust behavior.\n\n> **W2.Insufficient explanation of key concepts.**\n\n**R2:** We thank the reviewer for the valuable suggestion regarding the clarity of key concepts. In response, we will revise Section 3.1.1 to explicitly include the definitions of **Class Incremental Learning (Class-IL)** and **Domain Incremental Learning (Domain-IL)** in the main text, rather than placing them in the appendix. This adjustment aims to help readers quickly grasp the core task settings used throughout our experiments.\n\n- **Class-Incremental Learning:** A learning scenario where models sequentially learn tasks with shared data domains but new classes in each task, requiring adaptation to new classes without forgetting old ones.\n- **Domain-Incremental Learning:** A learning scenario where tasks involve fixed classes but varying data domains (e.g., different environments), demanding adaptation to domain shifts while maintaining recognition of all classes.\n\n> **W3. Missing citations in introduction from lines 55 to 85.**\n\n**R3:** Thank you for your detailed feedback. We fully agree that explicitly citing related work in the introduction is essential for academic rigor. In response to your comment regarding the paragraph discussing replay and regularization-based methods, we have revised the text to include specific citations based on the references already used in our paper. The updated version is as follows:\n\n“Memory buffer serves as a crucial training resource for many existing methods, particularly those based on replay [24, 41], as they necessitate the storage of previous samples or synthetic data for retraining. Additionally, some other methods relying on regularization [1, 34] also require buffering a portion of sample data or utilizing extra auxiliary datasets to facilitate the training process.”\n\n[1] Sara Babakniya, Zalan Fabian, Chaoyang He, Mahdi Soltanolkotabi, and Salman Avestimehr. A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks. Advances in Neural Information Processing Systems, 36, 2024.\n\n[24] Yichen Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Wenliang Zhong, and Guannan Zhang. Towards efficient replay in federated incremental learning. The Thirty-Fifth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), Seattle, USA, June 17-21, 2024.\n\n[34] Yuhang Ma, Zhongle Xie, Jue Wang, Ke Chen, Lidan Shou, and Luc De Raedt. Continual federated learning based on knowledge distillation. In IJCAI, pages 2182–2188, 2022.\n\n[41] Daiqing Qi, Handong Zhao, and Sheng Li. Better generative replay for continual federated learning. arXiv preprint arXiv:2302.13001, 2023.\n\n> **W4&Q1. Concerns about page limits and appendix usage.**\n\n**R4:** We thank the reviewer for highlighting the importance of structural clarity. We fully understand the conference's page limit requirements and agree that key content should be prioritized in the main text to ensure that readers can follow the paper's core contributions without needing to frequently consult the appendix. In response, **we will reorganize the division between the main text and the appendix**, ensuring that essential information is properly integrated into the main body, while strictly adhering to the length constraints. This will help improve the overall clarity and presentation quality of the paper."}, {"review_id": "w3iydtD3Ji", "reviewer": "Reviewer_SBXG", "summary": "This paper focuses on Federated Continual Learning (FCL) in resource-constrained scenarios. A large-scale benchmark involving six datasets and two incremental learning scenarios is constructed. Experimental results reveal that resource constraints substantially degrade the performance of existing FCL approaches. Based on experimental results, the paper summarized practical insights and future directions.", "questions": "1. The LLM foundation is a hot topic. Is there LLM based FCL methods? Are these methods applicable to the resource-constrained setting mentioned in this paper?\n2. This empirical study focuses on the image classification task. Do existing FCL methods work on NLP tasks?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-organized and the research topic is hot.\n2. It is timely and meaningful to study the resource constraints that affect the deployment of FCL.\n3. Extensive experiments and analysis are provided with significant workloads.\n4. The relative literatures are summarized well.\n5. This work may shed light on the future research directions for resource-friendly FCL.\n\nWeaknesses:\n1. Communication overhead is a key resource constraint in FL. The paper focuses on three specific resources. The authors should justify why other constraints, particularly communication overhead, were not selected for analysis.\n2. In Figure 1, some methods appear to underperform FedAvg even with sufficient resources, contradicting findings in their original papers. The authors should explain this discrepancy.\n3. The authors measure computational cost using gradient steps. As training time is a common alternative metric in FL, the authors should clarify their choice of gradient steps.", "was_revised": true, "final_justification": "After reading the rebuttal, I decide to keep my score.", "rebuttal": "> **W1. The authors should justify why other constraints, particularly communication overhead, were not selected for analysis.**\n\n**R1:** Thank you for your insightful comments. We fully agree that communication overhead is one of the core challenges in FL, especially in real-world deployments. In this work, we did not explicitly include communication cost as one of the three core resource constraints, mainly due to the following considerations:\n\n**Communication cost is fundamentally different from the client-side training constraints we focus on.** Our study primarily targets three types of **client-local resource limitations**: memory buffer, computational budget, and label rate. These are hard constraints that directly affect the training capacity of edge devices. In contrast, communication cost is mainly determined by factors such as communication frequency, model synchronization strategy (e.g., number of rounds $T$), and model size. **Therefore, it impacts training latency rather than the local training capability of clients.**\n\n**Communication constraints deserve dedicated treatment in future studies.** We appreciate your point that communication cost is an important dimension in FCL. For example, future studies could explore communication heterogeneity (e.g., bandwidth and latency across clients), the applicability of compression/sparsification techniques in FCL, or trade-offs between communication and local computation. As noted in Appendix E, we have already listed communication constraints as a direction for future work. In the current paper, we aim to first provide a systematic understanding of client-side resource limitations, which we believe is a necessary step toward building a more comprehensive performance analysis framework for FCL.\n\n> **W2. In Figure 1, some methods appear to underperform FedAvg even with sufficient resources, contradicting findings in their original papers.**\n\n**R2:** We thank the reviewer for the careful observation regarding the performance differences in the “resource-sufficient” setting. We understand the concern that some methods perform worse than FedAvg even when resources are sufficient, and we offer the following clarifications:\n\n1. **Inherent differences due to scenario adaptation**\n\n   Some methods (e.g., FOT and FedWeIT) were originally designed for specific settings, such as pure Class-IL with known task boundaries. In our benchmark, we enforce a unified protocol across both Class-IL and Domain-IL settings **without assuming task boundaries or task IDs**, which requires adapting these methods beyond their original design assumptions. This generalization may cause performance degradation on certain datasets.\n\n2. **Differences in the definition of “resource sufficiency”**\n\n   In prior work, “sufficient resources” often refers to a single unconstrained dimension (e.g., unlimited memory). In contrast, our benchmark defines the **resource-sufficient setting** as the baseline configuration where **memory buffer (M), computation budget (B), and label rate (R)** are all reasonably provisioned (see Table 4). For instance, generative replay methods in prior work often assume unlimited training iterations for generators. However, in our setup, generator training shares a fixed computational budget with the target model, potentially reducing sample quality and thus final performance.\n\n> **W3. The authors measure computational cost using gradient steps. As training time is a common alternative metric in FL, the authors should clarify their choice of gradient steps.**\n\n**R3:** Thank you for the question. We chose to use the number of gradient steps (i.e., the number of `backward()` calls) rather than wall-clock training time as a metric for quantifying computation cost, primarily to ensure experimental fairness. The reasons are as follows:\n\n**Gradient steps serve as a hardware-independent, architecture-agnostic, and more controllable indicator of computation cost.** Compared to wall-clock time, gradient steps offer several advantages:\n\n- **They are independent of hardware configuration** (e.g., GPU/CPU performance, memory size), which avoids variability in computation overhead caused by differences in deployment environments across methods;\n- **They are less sensitive to implementation details** such as the optimizer used or data loading mechanisms, making them more stable and reliable for cross-method comparisons;\n- **They provide direct controllability and reproducibility**: by specifying the maximum number of `backward()` calls, we can precisely bound the amount of client-side computation per communication round.\n\n> **Q1. The LLM foundation is a hot topic. Is there LLM based FCL methods? Are these methods applicable to the resource-constrained setting mentioned in this paper?**\n\n**A1:** We appreciate the reviewer’s insightful question regarding the integration of LLMs into FCL. LLMs have gained significant attention due to their strong performance on a wide range of tasks. However, their **substantial computational and communication overhead** poses major challenges for deployment on edge devices. Incorporating LLMs into resource-constrained FCL settings would require **a fundamental rethinking of model compression, distributed optimization, and continual learning strategies**.\n\nCurrently, most FCL methods focus on traditional architectures (e.g., CNNs, ResNets), which are more compatible with edge environments. We acknowledge that adapting LLMs to FCL under resource constraints is an important and promising direction. We plan to explore this line of work in future research, building on the foundation established in this study.\n\n> **Q2. This empirical study focuses on the image classification task. Do existing FCL methods work on NLP tasks?**\n\n**A2:** We thank the reviewer for raising the important question regarding natural language processing (NLP) tasks. To date, the vast majority of existing FCL methods have been proposed and evaluated in the context of image classification, while **systematic studies in NLP scenarios remain largely lacking**. Image classification tasks offer well-standardized data structures and widely used datasets, making it feasible to impose consistent resource constraints (M/B/R) and perform fair comparisons across methods. To ensure **comparability and reproducibility**, we selected image classification as the experimental foundation in this work. We agree that extending FCL benchmarks to NLP is of great value, and we plan to **systematically explore the design and evaluation of FCL under NLP scenarios** in future work, thereby enhancing the general applicability and practical relevance of our study."}]

Author rebuttal:

Meta-review: This paper provides a large-scale empirical study of federated continual learning (FCL) under resource constraints, including memory, computation, and label availability. By benchmarking a broad set of methods across six datasets and two task types, the authors show that existing approaches degrade significantly in constrained settings and offer insights into their limitations.

The work's strengths are its scope and relevance: over 1,000 GPU hours of experiments, a structured comparison across method families, and clear takeaways that distinguish FCL-specific challenges from centralized continual learning. One reviewer highlighted that systematic empirical studies are valuable for advancing the field.

The main weaknesses suggested were the lack of algorithmic novelty, limited comparison to CL under resource constraints (i.e., what makes FCL different?), reliance on relatively small-scale datasets, and omission of communication cost as a core resource. Some reviewers also found the chosen metrics (samples and gradient steps) less practical than MB or FLOPs.

Nonetheless, concerns were partly addressed in rebuttal and discussion. Reviewers who initially leaned negative acknowledged the clarifications and raised scores, while another was satisfied with revisions on clarity. The final balance of reviews supports acceptance, given the careful experimental depth and design.

For camera-ready, the authors should make sure to include updates per the discussion and final reviews (e.g., including the example of insufficient buffer size). The idea of incorporating communication constraints into the evaluation should be added in the discussion section. All other discussions should be incorporated or clarified in the final version.
Decision: Accept (poster)