Class-wise Balancing Data Replay for Federated Class-Incremental Learning
Authors: Zhuang Qi
Ying-Peng Tang
Lei Meng
Han Yu
Xiaoxiao Li
Xiangxu Meng
Venue: NeurIPS 2025 oral ()
Keywords: Federated Learning;Federated Class-Incremental Learning; Data Replay
Primary area: general_machine_learning

Abstract: Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay has become a promising solution, which can alleviate forgetting by reintroducing representative samples from previous tasks. However, their performance is typically limited by class imbalance, both within the replay buffer due to limited global awareness and between replayed and newly arrived classes. To address this issue, we propose a class-wise balancing data replay method for FCIL (FedCBDR), which employs a global coordination mechanism for class-level memory construction and reweights the learning objective to alleviate the aforementioned imbalances. Specifically, FedCBDR has two key components: 1) the global-perspective data replay module reconstructs global representations of prior task knowledge in a privacy-preserving manner, which then guides a class-aware and importance-sensitive sampling strategy to achieve balanced replay; 2) Subsequently, to handle class imbalance across tasks, the task-aware temperature scaling module adaptively adjusts the temperature of logits at both class and instance levels based on task dynamics, which reduces the model’s overconfidence in majority classes while enhancing its sensitivity to minority classes. Experimental results verified that FedCBDR achieves balanced class-wise sampling under heterogeneous data distributions and improves generalization under task imbalance between earlier and recent tasks, yielding a 2%-15% Top-1 accuracy improvement over six state-of-the-art methods.

Class-wise Balancing Data Replay for Federated Class-Incremental Learning
Zhuang Qi', Ying-Peng Tang?, Lei Meng".; Han Yu², Xiaoxiao Li Xiangxu Meng'
Schooo of Software, Shandong University, China
"College of Computing and Data Science, Nanyang Technological University, Singapore Department of Electrical and Computer Engineering, University of British Columbia, Canada Vector Institute, Canada
z_qi @mail.mammmmmmmmmmmmmm...a ada.l..du.... sdu..duueee edu. en, yingpeng- tang@ntu. brngdnntu.du..se...... sdu 1meng@sdu. edu. cn, han. yuentu. edu.sg, xiaoxiao li@ece wbc. ca, mxx@sdu. edu.
Abstract
Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay has become a promising solution, which can alleviate forgetting by reintroducing representative samples from previous tasks. However, their performance is typically limited by class imbalance, both within the replay buffer due to limited global awareness and between replayed and newly arrived classes. To address this issue, we propose class-wis wise balancing data replay method for FCIL (FedCBDR), which employs global coordination mechanism for class-level memory construction and reweights the learning objective to alleviate the aforementioned imbalances. Specifically, FedCBDR has two key components: 1) the plobal-perspeede data replay module reconstructs global representations of prior task in privacy-preserving manner, which then guides a class-aware and importance-sensidive sampling strategy to achieve balanced replay; 2) Subsequently, handle class imbalance across tasks, the task-aware temperature scaling module adaptively adjusts the temperature of logits at both class and instance levels based on task dynamics, which reduces the model's overconfidence in majority classes while enhancing its sensitivity to minority classes. Experimental results verified that FedCBDR achieves balanced class-wise sampling under heterogeneous data distributions and improves generalization under task imbalance between earlier and recent tasks, yielding 255% Top-1 accuracy improvement over six state- of -htth-rrt art methods.
Introduction
Federated learning (FL) is a distributed machine learning paradigm that enables collaborative training of shared global model across multiple data sources [1] 2 3 5]. It periodically performs parameter-level interaction between clients and the server instead of gathering clients' data, which enhance data privacy while leveraging the diversity of distributed data sources to build more generalized global model 66
I0 [1.. This mechanism makes widely applicable to various fields [12 [13] [4] [15] [6. Building upon this foundation, Federated Plass-Incremente Learning (FCIL) extends FL by introducing dynamic data streams where clients sequentially encounter different task classes under non-independent and identically distributed data [17
7
S
18
19
20 21). However,
this amplifies the inherent complexities of Fl., as the global model must integrate heterogeneous and
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Local view Data Selection
Local Pseudo Feature Generation
Global-panppecthe
Raplay Result
Clent
Personallzed Informative
Importance
Cliett
Global
Corprdharsie
Paeudo festure
Balanced Class Sample Counts
1. Unbalaaced Class wise
Client
Local
Informative Model
Personalioed
Importence
Scare
Sample Counts
Client
Data
Local
Global Model
Pscudo Feature
Local
Preudo Feature
Global
(x) Traditional Dats Replay Strategg
[b] Gisbas--arnpecctiee Data Replay Strateng
Figure 1: Motivation of the FedCBDR. Traditional data replay strategies typically focus on local information and, due to the lack of global awareness, often result in imbalanced class distributions during replay. FedCBDR aims to explore global information in ppeeec--rreeeiing manner and leverage it for sampling, which can alleviate the class imbalance problem.
evolving knowledge from clients while mitigating catastrophic forgetting, despite having no or only limited access historical data [2
To address the challenge of catastrophic forgetting in FCIL, data replay has emerged as a promising strategy for retaining knowledge from previous tasks. Existing replay-based methods can be broadly categorized into two types: generative-based replay and exemplar-based replay. The former leverages
generative models synthesize representative samples from historical tasks (3
25 26
Its core
idea to learn the data distribution of previous tasks and internalize knowledge in the form of model parameters, enabling the indirect reconstruction of prior knowledge through sample generation when needed [19 277. However, they often overlook the computational cost of training generative models and are inherently constrained by the quality and fidelity of the synthesized data 23 29]. In contrast, exemplar-based replay methods directly store real samples from previous tasks, avoiding the complexity of generative processes while leveraging high-quality raw data to ensure robust retention of prior knowledge [17] (2) 30 B1). These methods rely on a limited set of historical samples to maintain the decision boundaries of previously learned task classes. However, due the lack of global perspective on data distribution across clients, these methods are prone to class-level imbalance replayed samples, which undermines the model's ability to retain prior knowledge 30
To address these issues, this paper proposes a class-wise balancing data replay method for FCIL, termed FedCBDR, which incorporates the global signal regulate class-balaneed memory construc- tion, aiming to achieve distribution--warr replay and mitigate the challenges posed by non-lID client data, illustrated in Figure TT. Specifically, FedCBDR comprises two primary modules: 1) data replay (GDR) module reconstructs representation of historical tasks by leveraging feature space decomposition, which enables effective cross-client knowledge integration while preserving essential attributes information. Furthermore, it introduces principled inportance-driven selection mechanism that enables class-balanced replay, guided by globally-informed understanding of data distribution; 2) the task-aware temperature scaling (TTS) module introduces multi-level dynamic confidence calibration strategy that combines task-level temperature adjustment with instance-level weighting. By modulating the sharpness of the softmax distribution, balances the predictive confidence between majority and minority classes, enhancing the model's robustness to class imbalance between historical and current task samples.
the
srivacy-preserving pseudo global
Extensive experiments were conducted on three datasets with different levels of heterogeneity, including performance comparisons, ablation studies, in-depth analysis, and case studies. The results demonstrate that FedCEDR effectively balances the number of replayed samples across classes and alleviates the long-tail problem. Compared to six state-of-the-an existing methods, FedCBDR achieves a 15% Top-1 accuracy improvement.
2 Related Work
2.1 Exemplar-based Replay Methods
In FCII,, exemplar-based replay methods aim to mitigate catastrophic forgetting by storing and replaying a subset of samples from previous tasks. They typically maintain a small exemplar buffer on each client, which is used during training alongside new task data to preserve knowledge of

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For example, GLFC alleviates forgetting in FCIL by leveraging local exemplar buffers for rehearsal. while introducing class-aware gradient compensation and prototype-guu -eiidpeeegddedddeeeee global coordination jointly address local and global forgetting
previously learned classes [17 30 31
32
33
34]
17 Moreover, Re-Fed introduces a Personalized Informative Model to strategically identify and replay task-relevant local samples, enhancing the efficiency of buffer usage and further reducing forgetting in heterogeneous client environments [30]. However, the lack of global insight in local sample selection often results in class imbalance, while the long-tailed distribution between replayed and current data is frequently overlooked, degrading the effectiveness of data replay [30
2.2 Generative-based Replay Methods
Generative replay methods aim to reconstruct the samples of past tasks through techniques such as generative modeling 19 23 27 35 36), which enables the model to revisit historical knowledge to mitigate catastrophic forgetting. Following this line of thought, TARGET generates pseudo features through a globally pre-trained encoder and performs knowledge distillation by aligning the current model's 's predictions with those of a frozen global model 277 LANDER utilizes pre-trained semantic text embeddings as anchors to synthesize meaningful pseudo samples, and distills knowledge by aligning the model's predictions with class prototypes derived from textual descriptions [[9]. However, these methods are typically limited by the high computational cost of training generative models and the suboptimal performance caused by low-fidelity pseudo samples 19 27
2.3 Knowledge Distillation-bass Methods
Knowledge distillation- based methods generally follow two paradigms. The first focus om aligning the output predictions of the current model with those of previous models, which aims to preserve task-specific decision boundaries (37 38
45 The second estimates the
importance of model parameters for previously learned tasks and performs regularization to prevent forgetting [46] 47). Both approaches avoid storing raw data but are prone knowledge degradation
over time, especially as the number of tasks increases 37 46
3 Preliminaries
We consider a federated collaboratively train a global model with the assistance of K distributed elients. Each client receives a sequence of classification tasks {D( new classes. Upon the arrival of task the global model parameters Og are optimized to minimize
learning (FCIL) setting, where central server aims to
(D2) ..... D(d) }. where each task introduces a disjoint set of
the average loss over the union of all samples seen so far, i.e., D = U U D( by solving
ming J Ei=1 EA N
()
In replay-based -basd methods, each client maintains memory buffer with fixed budget of M samples. When task / arrives, the client selects up to N representative samples from each of the previous tasks {1, [1....,e 1}, subject to the total memory constraint. The resulting memory set is denoted k,
by B (((-1) - (x] ( where N is the number of samples stored per task and (((-1)
satisfies |B8--1 S M. The local training set on client k then becomes D = ( U B((- combining current and replayed samples. Based on these local datasets, the server updates the global
model by minimizing the aggregated loss: ming Σ k= (x.,))E (I]x(x;)),1))
4 Class-wise Balancing Data Replay for Federated Class-Incremental Learning
This section presents an effective active data selection method for FCIL, which aims to explore global data distribution to balance class-wise sampling. Moreover, it leverages temperature scaling to adjust the logits, which can alleviate the imbalance between samples from previously learned and newly introduced tasks. Figure 2 and Algorithm iillustrates the framework of the proposed FedCBDR.
3

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Client
Client
Local Training
Local Model Aggregation
Local
Local Model
TTS (s > 1)
Data
Current
Local Training
Task Sample
Replay
Cached
Task
Sample
LEF 1)
Local Models
Global Model
Tasks
Tesk st1
Global perspective Replay
Sample
X
X
Cached Sample
Features
Pseudo
SVD
2 Pscudo Feature Construction
X;
X
Cathed Data Indlices
Stups 312
Task
Task 5+1
Figure 2: Illustration of the FedCBDR framework. It first trains local models using samples from current and previous tasks. After fixed number of communication rounds, each client extracts local sample features using the global model and applies inverse singular value decomposition (ISVD) to obtain pseudo features. The server then aggregates both local models and pseudo features, performs SVD on the features, and selects representative samples based on leverage scores. The corresponding sample indices are sent back to the clients for balanced replay.
4.1 Global-perspective Data Replay (GDR)
Due to privacy constraints, traditional data replay strategies typically rely on local data distributions. However, the absence of global information often leads to class imbalance in the replay buffer. To address this, the GDR module aggregates local informative features into global pscudo feature set, enabling exploration of the global distribution without exposing raw data.
we first generate set random orthogo- for each client & and task i, and globally shared matrix Q(1) Rdxd where d denotes the dimension of the feature. Each client encrypts its local feature matrix x(() = My (D)
Inspired by Singular Value Decomposition (SVD) 48 49],
nal matrices: client-specifie matrix p(()) E TR D [
(() via Inverse Singular Value Decomposition (ISVD):
x
(1) p x Q
(I)
and uploads the encrypted matrix (0) to the server, where Ms(.) is the feature extractor of the global model. The server then aggregates all encrypted matrices into global matrix X
X concat ((0))
I,..,,k,, K),
and performs SVD as follows:
X = U60 "00'")00)00) V('T
where U6)' _*x" 200' e Rad and ViV Rddd with denoting the total number of samples from all clients. Next, the server extracts a submatrix of the left singular vectors for each client k by:
U Ia (V E 18 pPP(px
where I+(-) denotes a row selection function that returns the indices corresponding to client k's samples. To quantify the importance of local samples within the global latent space, client & computes for j-th sample of task as:
a leverage score [50 51 52
Ao -== - (1)
(5)
where Gi,j denotes the j-th standard basis vector in task i. Notably, a higher leverage score in- dicates that the sample has a larger projection the low-dimensional latent space, suggesting that it contributes more significantly to the global structure and is more representative. More- over, clients send their leverage scores to the server, which aggregates them into global vector -' concat(Ti) lk = L,... Kij- I,...,nij n and normalizes it to obtain a sampling distribution:
Pk
6-
TK
(6)
Σ
3
j

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Algorithm FEDCBDR
1: Initialize: R: number of communication rounds; K: number of clients; t: number of tasks; 0g:
global model parameters; " replay buffer for historical tasks client k; D local data of task on client
2: for each task 1 to t do
3: for each communication round to R do K do
4:
for each client & 5: Initialize local model parameters: 0₂ +- 0g
6: if s then
7:
Sample a mini-batch Ç from k and update 0₂ using Eq. 8 else
8:
9:
Store the historical task data corresponding globally sampled IDs into B
Sample a mini- bati-batch Ç from D(.) B and update 8g using Eq. Compute pseudo- -eaturess based Eq. T and upload them to the server.
10:
11:
12:
end end for
13:
14:
if r < R then
15:
Aggregate local model parameters across clients.
16:
else
Aggregate model parameters and pseudo-feature from all clients using Eq. 2 Perform Global Sampling based on Eqs. 3 and send the selected sample IDs back to the corresponding clients.
18:
end if end for 21: end for
19:
// Global Sampling Procedure
23: Form the global feature pool X by aggregating all pseudo-feature via Eq. (2)
Perform singular value decomposition (SVD) using Eq. to extract key attributes.
25: Compute leverage scores for each client's samples using Eqs. 5 and normalize globally using
Eq. 6
26: Pertorm sampling and adjust the probabilities of the selected samples accordingly.
Subsequently, we perform i.i.d. sampling based on the distribution p = [pk 1,.., K;j ..., n Once sample x is selected, its sampling weight adjusted /n., Ez. where nt, denotes the number of selected samples and ps is the original sampling probability of x, ez is the standard basis vector of This adjustment ensures unbiased estimation during aggregation. Following the sampling procedure, the server communicates the selected sample indices to their respective clients, where the corresponding data points are subsequently marked for further use.
4.2 Task-aware Temperature Scaling (TTS)
Due to limited replay budgets, samples from previous tasks are often much fewer than those from the current task, leading to class imbalance and poor retention of past knowledge. To mitigate this, the TTS module dynamically adjusts sample temperature and weight based on task order, enhancing the contribution of tail-class samples during optimization.
Specifically, we use a lower temperature to sharpen logits for samples from earlier tasks. Furthermore, to further amplify the optimization effect of tail-class samples during training, we also leverage re-weighted cross-entropy loss, i.e.,
CyTS
CE Softmax Connctt
CB
Softmax
Coucas
Tead
Tame
Newe
Tud
where Noa and Neew denote the number of samples from the previous and newly arrived task, respectively; yi and yز are the ground-truth labels; =1 and z!oww denote the logits corresponding to old classes and new classes, respectively; Told and Tnew are the temperature scaling factors for previous and newly arrived task samples; Wold and Wnew are the corresponding sample weights; CE(.) denotes
5

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Table 1: Statistics of the datasets used experiments.
Federated Settings
Datasets
#Class
#Training
#Testing
Image Size
Clients
Tasks
Heterogeneity
CIFARIO
10
50,000
10,000
32 x 32
5/10
3/5
0.5/1.0
CIFARI00
100
50,000
10,000
32 x 32
5/10
5/10
0.1/0.5/1.0
TinylmageNet
200
100,000
10,000
64 64
5/10
10/20
0.1/0.5/1.0
the erosspyntropy loss function; and Softmax(z/r) is the temperature-scale softmax function used to adjust the sharpness of the output distribution.
4.3 Training Strategy
The training strategy consists of two stages to progressively address the evolving challenges in federated class-incremental learning. Algorithm presents the pipeline of the FedCBDR.
Stage 1:
Initial Task Optimization. In the first task, client k learns from local data using the standard cross-entropy loss, i.e.,
1 Σ CE(y, Softmax (x:))), N
min Un
(8)
Stage 2: Llass-nncremental Optimization. As new tasks arrive and class imbalance emerges between previous and current tasks in client k, we employ CrTs to mitigate the imbalance, i.c.,
mins, E now Sun--E
Suttmax
Concat
(x.)
CE Softmas
Courat
Am
where æ; is the input sample, y4 the corresponding ground -tuth, fal (z) and fnew (x) represent the outputs of the model corresponding to old and new classes, respectively. Softmax(() converts the logits into a probability distribution.
5
Experiments
5.1 Experiment Settings
Datasets.
Following existing studies 27
30
conducted all experiments three commonly
used datasets, including CIFARIO 53 54, CIFAR100 [53] 54 and TinylmageNe (55) to validate
the effectiveness of the FedCBDR. We simulate heterogeneous data distributions across clients using the Dirichlet distribution with parameters 3 = 10.11 0.5, 1.0), where smaller values of correspond
to higher level of data heterogeneity. The statistical details presented in the Table T
Evaluation Metric. Following prior studies 19
56 57 58), we adopt Top-1 Accuracy as the
evaluation metric, defined as Accuracy Neorreat/ /N Nooual where Neorrect and Niotal denote the number of correct predictions and the total number of samples, respectively.
Implementation Details. In the experiments, the number of clients is fixed K 5, with each client running local epochs E per round, using batch size B = 128. For all datasets, we adopt ResNet-18 as the backbone, with the classifier's output dimension dynamically updated as tasks progress and conduct 7' = 100 communication rounds per task. The SGD optimizer is employed with learning rate of 0.01 and a weight decay of 10 The number of stored samples per task varies by dataset and split setting: for CIFAR10 10, 450 samples are stored under 3-task splits and 300 under 5-task splits; for CIFAR100. 1,000 samples are used for 5-task splits and 500 for 10-task splits; for TinylmageNet, 2,000 samples are stored for 10-task splits and 1. 1,000 for 20-task splits. For the temperature and weighted parameters, we select Fold E {0.8, 0.9} and wald E (1.1, 1.2, 1.3, 1.4) for previous tasks, while Tacw E 1.2] and Wnew E (0.7. 0.8, 0.9) are used for newly arrived tasks. Moreover, the hyperparameters of baselines are tuned based on their original papers for fair comparison. And training on each client is performed using NVIDIA RTX 3090 GPU (24 GB).

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Table 2: Performance comparison between FedCEDR and baselines across three datasets under varying levels of heterogeneity (3). CIFARIO divided into 3 tasks, CIFARI00 into tasks, and TinylmageNet into 10 tasks. All methods were executed under three different random seeds, and both the mean and standard deviation of the results are reported. The best results are bolded.
CIFARIO
CIFARIO0
Tiny ImnyaageNeet
Method
B-0
B=1.0
B=0.1
B-0.5
3=1.0
3=0.1
3=0.5
B=1.0
Finetune
38.71 13.7
40.49
13.0
15.17
16.75126
17.15
6.06
6.00
6.40
C
39.93 42.70
18,30
20.7015.3
21.22
6.30
6..4
7.3610.6
FedL.wF
56.03
58.29
33.97.26 37.0913.1
41.91
11.81
11.47.1.0
14.87
TARGET
44.17+4.4
54.49+4.5
30.15+36
33.47+4.
35.25+2.0
10.71
10.18+0.9
12.49+1.1
LANDER
53,90
60.79+1.4
44.07
47.6343.7
52.77+1.4
13.80
15.02+1.9
16.36+1.0
Re-Fed
53.4643.5
60.7344.3
32.6743.7
38.4242.9 45.28
15.73+1.7
15.93+1.a
16.05
FedCBDR
11.1 65.20 46.40
49.7642.7 52.0641.5
18.3741.3
18.8640.9
18.7840.9
Table 3: Performance comparison between FedCBDR and baselines across three datasets under
varying levels of heterogeneity (3). CIFARIO is divided into 5 tasks, CIFAR100 into 10 tasks, and Tmay into 20 tasks. All methods were executed under three different random seeds, and both the mean and standard deviation of the results are reported. The best results are bolded.
CIFARIO
CIFARI00
Tiny ImageNet
Method
0-5
3-1.0
8-1.1
3-0.5
3-1.0
3-0.1
3-0.5
3=1.0
Finetune
19.7842.a
23.34
7.22
9.39
9.64
3.40
3.73
3.95
FedEWC
20.11 28.97
8.08
11.694n.7
12.1941.7
3.50
4.58
5.08
FedLwWF
38.7642.a
52.95.3.11
18.7341.1 25.30
28.21
3.6710.4
6.61
10.8
10.22
TARGET
35.27+1.7 48.28+1.2
13.61 21.09+0.1
24.22+1.1
5.32+0.6
5.39
5.72+0.5
LANDER
40.22+2.4
58.07+3.4
27.79+19
33.51+23
37.42+1.8
8.89+0.6
8.57
10.45+0.6
Re-Fed
54.94
58.1942.5
29.33+1.3
39.5441.3
40.96+1.1
9.36+0.9
11.44
12.27+1.1
FedCBDR
61.18+1.3 65.42+
45.11 46.51+1.
47.79+1.4
12.5840.4
14.47
15.6940.6
5.2
Performance Comparison
To evaluate the effectiveness of the proposed FedCBDR, we compare it with six representative baseline methods: Finetune [19], FedEWC [46), FedLwF (37), TARGET (27, LANDER [19], and Re-Fed
30 As reported in Table and Table 3 the results can be summarized as follows:
FedCBDR achieves the highest Top-1 accuracy in most cases across the three datasets under varying levels of heterogeneity and task splits. The only suboptimal result occurs on CIFAR with tasks and 3 1.0, where FedCBDR (52.06%) performs slightly worse than LANDER (52.77%). This demonstrates the adaptability and robustness of the proposed FedCBDR across complex settings.
Despite LANDER attains the best performance on CIFAR 100 under the 5-task and 3 1.0 setting, demands the generation of more than 10,000 samples per task, and the overhead of training its data generator surpasses that of the federated model, raising concerns about its scalability.
Knowledge distillation- based methods like FedLwF perform well on simpler tasks (CIFARIO) by using pretrained knowledge to guide local models. However, their performance drops on more complex or heterogeneous tasks due limited adaptability local variations.
Given an equal memory budget, class-balanced sampling (FedCBDR) consistently achieves supe- rior performance compared class-imbalanced strategy (Re-Fed), as ensures more equitable representation across categories and effectively mitigates class-level forgetting in FCIL scenarios.
5.3
Ablation Study
In this section, we conducted an ablation study to investigate the contributions of key modules, including the -eosaalperspeetive Active Data Replay (GDR) module and the Task-aware Temperature Scaling (TTS) module. Table 4 4resents the results, which can be summarized as follows:
7

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Table 4: Ablation results under different levels of data heterogeneity and task splitting settings.
"3/5/10" denotes CIFARIO with tasks, CIFARIOU with tasks, and TinylmageNe with 10 tasks; "5/10/20" represents 10, and 20 tasks respectively.
Task Splitting
CIFARIO
CIFAR100
InnIImgeNNet
Method
0-0.5
8-1.0
3-0.1
0-0.5
3-1.0
B-0.1
3-0.5
8-1.
Finetune
38.71
40.4943.0
15.17:2.
16.75
17.15
6,0610.9
6.00
6.40
+GDR
62.13 63.
45.28
47.66.0.9
51.47
17.244..6
17.89
18.0440.4
3/5/10
+TTS
41.34 42.55
17.32.0.5
17.14.0.4
19.32.0.5
6.67
6.92.0.3
7.27
+GDR+TT
64.11:1.2 65.20
46.40:166
49.76
52.06+1.5
18.37
18.86
18.78
Finetune
19.78 23.34
7.22+111
9.39_0.7
9.64
3.40
3.7320.5
3.9510.3
+GDR
59.3413.1 63.20
44.0441.3
46,33
46.50
11.44
13.8520.5
14.5140.6
5/10/20
+TTS
22. 22,43
8.31
10.21
10.33
3.78
4.04.0.4
4.1610.
+GDR+TTS
61.18 65.42_1.8
45.11
46.51
47.49
12.58+0.4
14.47
15.69
Incorporating the GDR module substantially improves performance across all cases, particularly under high data heterogeneity (B 0.1), demonstrating its effectiveness in alleviating catastrophic forgetting even with a limited number of replay samples in federated class- incremental learning.
Using the TTS module alone leads to consistent improvements over Finetune, highlighting its effectiveness in addressing intra-client class imbalance through temperature scaling. This contri- bution to better generalization is particularly evident under the more challenging "5/10/20" task splitting scenario.
The integration of both modules results in the best overall performance, consistently achieving the highest Top-1 accuracy across various datasets and heterogeneity levels. This stems from their complementary strengths: the GDR module mitigates inter-task forgetting, while the TTS module alleviates both intra- and inter-client class imbalance.
5.4
Performance Evaluation FedCBDR under Incremental Tasks
CIFAR10
CIFAR10
CIFAR100
CIFAR100
TinylmageNet
TinylmageNet
80
50
Accuracy
60
75
Test Acturaty
Ret Acturacy
Rest Accurtt
Accuracy
50
40
60
50
40
20
20
20
0
1
2
01 2
4 5 8
0
1
2
4
888
5
10
15
Task ID
Task
Task
ID
Task 10
Task 1D
Task ID
Figure 3: Performance comparison of all methods across varying task splits on CIFARIO (3/5 tasks), CIFAR100 (5/10 tasks), and TinylmageNet (10/20 tasks) with 3 = 0.5.
This section investigates the performance of FedCEDR and the baselines in incremental cases on three datasets. pressets the average accuracy of methods on both current and previous tasks. Notably, FedCBDR consistently outperforms other baseline methods across all task splits, with its accuracy curves remaining higher throughout the incremental process. Furthermore, FedCBDR exhibits slower performance degradation the number of tasks increases, indicating stronger resistance to catastrophic forgetting. In addition, it maintains significantly higher accuracy on later tasks, especially in challenging settings such as CIFAR100 and TinylmageNet with 10 tasks, highlighting its ability to balance knowledge retention and adaptation to new classes.
5.5 Quantitative Analysis of Replay Buffer Size on Test Accuracy
In this section, we evaluate the performance of Re-Fed and FedCBDR under different buffer size M settings, and additionally include LANDER, which generates 10,240 synthetic samples for each task. As shown in Table FedCBDR exhibits more significant performance ad advantages over Re-Fed under limited memory settings, and even surpasses LANDER, which relies on a large-scale generative replay buffer. Furthermore, as the buffer size increases, FedCBDR demonstrates more stable and significant performance improvements. This indicates that the method can effectively leverage larger replay buffers for continuous optimization. However, Re-Fed exhibits noticeable performance fluctuations
8

---

Table Comparison of model performance with varying memory size M across datasets.
CIFAR10
CIFAR100
TinyImageNet
Methods
M=150
M=300
M-450
M=500
M=1000 M=1500
M=2000 M-2500
LANDER (10240)
52.90
47.05
14.77
Re-Fed
47.23
53.47
54.66
33.89
38.42
47.84
15.89
16.78
FedCBDR
51.99
59.02
63.81
40.12
49.66
55.94
18.33
19.41
under small and medium buffer settings. In particular, its accuracy is significantly lower than that of FedCEDR CIFAR 100 with M = 500 and TinyImageNet with M 2000, indicating its limited ability to mitigate inter-class interference and retain knowledge from previous tasks. These findings validate that, under the same buffer budget, balanced sampling distribution more effective than imbalanced one in alleviating forgetting and improving overall model performance.
5.6 Sensitivity Analysis of FedCBDR on Temperature and Weighted Hyperparameters
200
Figure 4: Performance of FedCBDR on CIFARI00 (3 = 0.5, 5-task split) under varying temperature and weighted (ward E {1.1, 1.5, 2.0}, Wnew
(Told E {0.5, 0.7, 0.9}, Tnow E (0.5, 0.7, 0.9)) settings.
Figure gives sensitivity analysis of FedCBDR with respect temperature and sample weighting hyperparameters. Overall, temperature scaling and sample re-weighting help mitigate class imbalance, but model performance varies considerably with different hyperparameter settings. The model achieves better overall performance when Wold = 1.1, Whew = 0.9, Told = 0.9, and Tnew = 1.1. This is because slightly higher weight and temperature for previous-tass samples help retain old knowledge, while lower weight and higher temperature for newly arrived samples reduce overfitting and improve adaptation. However, inappropriate hyperparameter choices may harm performance. For instance, large Trew (e.g., 2.0) leads to overly smooth predictions, reducing discrimination among newly arrived classes. These results emphasize the need for proper tuning to ensure balanced learning.
5.7 Comparison of Per-Class Sample Distributions in the Replay Buffer
Tosk
Tosk
Tosk
Task 4
Anpoge
100
125
200
50
Nimber
50
40
Class Indee
Closs Idt
Clss Indes
Cass In6er
Figure 5: Comparison of per-class sample distributions in the replay buffer between FedCBDR and Re-Fed on the CIFAR 100 dataset, under a heterogeneity level of 5 0.5 and 5-task split case.
To evaluate the effectiveness of FedCBDR in balancing class-wit wise sampling, Figure 5 illustrates the per-class sample distributions in the replay buffer between FedCBDR and Re-Fed across different task stages. Overall, across different task stages, FedCBDR (orange bars) exhibits per-class sample distribution that is consistently closer to the average level (red line), whereas Re-Fed shows noticeable skewness and fluctuations. This indicates that FedCBDR is more effective in achieving balanced class-wir wise sampling in the replay buffer. In addition, FedCEDR ensures that no class is overlooked

---

during sampling, while Re-Fed may fail to retain certain classes in the replay buffer- example, class 79 is missing in Task 4 under Re-Fed. This highlights the robustness FedCBDR in maintaining class coverage throughout incremental learning.
5.8
Visualization of Model Attention and Temperature-aware Logits Adjustment
Input Images
[e] Dog
Bird
Airplane
Cat
Deer
Ship
Frog 3.402
Ponse 3.839
Frog
4.320
Truck 2.17
Autom 3.40
Trudk 2.500
Autom
Track
Frng 1.207
Frog
Bidd 4.17
Alpp
4.725
Ct
4.66
Ce 138077
Dear
0.00%
Dcg 13864
+GOR
Dog
Bag
Deg
Ship
Honse
Ridd 4.677
4.000
3.608
.GBR +TS (W)
Frog 2.29
Ship 2.40
Doer 2.077
Brd 1..20
Frog
12395
18268
Siis 2:585
Dog
2335
Frgg 1372
Truck 0.482
Brd
11230
Truck 1.688
+GDR (T15
Bid 3.884
4.008
4.350
Deer 4.284
44000
ship 2.557
Homs 1230
Truck 2.035
Stip 3.002
Dog 13211
Brd
13.000
Rutton 2.374
1.000
22.372
Fing 2.000
Frog
Figure 6: Case studies of model attention and the effect of temperature-awaar logits adjustment on CIFAR10 (3 0.5, 3-task split).
This section presents case studies comparing prediction confidence and attention focus using Grad- CAM [59 60
visualizations. As shown in Bigure/ja-u in the absence of data replay, the model struggles to correctly classify samples from previous tasks and fails to attend to the relevant target regions. The incorporation of data replay in FedCEDR alleviates this issue by correcting predictions and guiding attention back to semantically important areas. Despite partially mitigating forgetting, data replay alone may still lead misclassification classes with limited samples. The integration of temperature scaling (T) and sample re-weighting (W) in the TTS module enables the model to better distinguish confusing classes through temperature adjustment, improving tail class accuracy and enhancing prediction stability, as depicted in Figure d-f-). These findings demonstrate the crucial role of the collaboration between both modules in mitigating knowledge forgetting during incremental learning.
61 62 63
low-confidence predictions for tail
6 Conclusions and Future Work
To address the challenge of inter-class imbalance in replay-based federated class- maas-innrrmmetal learning. we propose FedCBDR that combines class-balanced sampling with loss adjustment to better exploit the global data distribution and enhance the contribution of tail-class samples model optimization. Specifically, it uses SVD to decouple and reconstruct local data, aggregates local information in srivacy-preserving manner, and explores i.i.d. sampling within the aggregated distribution. In addition, it applies task-aware temperature scaling and sample re- re-eeighting to mitigate the long-tail problem. Experimental results show that FedCEDR effectively reduces inter-class sampling imbalance and significantly improves final performance.
Despite the impressive performance of FedCBDR, there remain several directions worth exploring address its limitations. Specifically, we plan to investigate lightweight sampling strategies to reduce feature transmission costs in FedCBDR, and develop more robust post-sampling balancing methods that mitigate class imbalance with less sensitivity to hyperparameters extending FedCBDR to more complex scenarios 69
64 65 66,, 67]. Moreover, a promising direction.
Acknowledgments
This work is supported in part by the Key Research and Development Program of Shandong Province (Grant No. 2024TSGC0667) and the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG101/24).
10

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14

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A Appendix
A.1
Experimental Results
A.1.1 Performance Comparison
To thoroughly verify the effectiveness of the proposed FedCBDR, we compare its performance against various baselines under the setting of 10 clients. Based the original implementations, we generate 10,240 synthetic samples per task for both TARGET and LANDER. The data replay configurations for Re-Fed and FedCBDR follow the settings outlined in Section 5.1 The results presented in Tables 6 and 7
Consistent with the results shown in Tables 2 and FedCBDR achieves the best performance across all cases. Notably, FedCBDR achieves over a 10% gain compared the second-best performing method in several settings.
Table 6: Performance comparison between FedCBDR and baseline methods across CIFAR-10, CIFAR- 100, and TinylmageNet under varying levels of data heterogeneity (Dirichlet parameter 3). Specifi- cally, CIFAR-10 is split into 3 tasks, CIFAR-ION into 5 tasks, and TinyImageNe into 10 tasks. The number of clients fixed at 10, and all experiments are conducted with a random seed of 2023 to ensure reproducibility. The best results bolded.
CIFAR1O
CIFAR100
Tiny ImageNet
Method
B=0.5
B-1.0
3-0.1
0-0.5
3=1.0
8=0.1
8=0.5
B=1.0
FedEWC
36,40
42.00
15.19
18.66
19.50
6.19
7.23
7.78
FedLwF
48.24
49.11
27.02
37.92
41.77
10.67
13.02
14.73
TARGET
38.23
41.11
18.34
23.59
25.71
7.45
8.29
8.87
LANDER
41.54
45.52
30.83
43.69
47.29
12.33
15.18
15.64
Re-Fed
45,49
52.22
31.81
36.40
37.95
9.28
11.48
12.10
FedCBDR
59.80
62.59
42.25
47.90
48.55
14.81
16.54
17.43
Table 7: Performance comparison between FedCBDR and baseline methods across CIFAR-IO, CIFAR-
100, and IinylmageNet under varying levels of data heterogeneity (Dirichlet parameter 3). Specifi- cally, CIFAR-10 is split into 5 tasks, CIFAR-100 -000 into 10 tasks, and TinyImageNet into 20 tasks. The number of clients is fixed at 10, and all experiments are conducted with random seed of 2023 to ensure reproducibility. The best results are bolded.
CIFAR10
CIFAR100
Tiny ImnylmaeeNet
Method
0-5.5
3=1.0
3-0.1
3=0.5
8=1
3=0.1
-=0.5
3=1.0
FedEWC
20.18
23.33
6.68
10.98
12.30
3.27
4.80
4.89
FedLwwF
43.31
46.79
13.82
17.79
27.80
4.50
5.71
9.07
TARGET
21.60
28.39
12.11
16.64
17.14
3.45
4.88
5.01
LANDER
27.24
32.21
10.74
25.87
31.79
4.74
12.05
13.21
Re-Fed
38.28
39.22
28.08
33.52
37.27
7.95
8.53
10.13
FedCBDR
51.71
59.57
37.42
43.82
45.50
11.51
14.45
15.25
A.1.2 Performance Evaluation of FedCBDR under Incremental Tasks
We evaluate the performance evolution of FedCBDR and competing methods under 10-client setting across incremental tasks on three benchmark datasets. Specifically, CIFAR-10 split into 3 tasks
(8 {0.5, 1.0)), CIFAR-100 into 5 tasks (8 {0.1, 1.0)), and TinylmageNet into 10 tasks
(3 (0.1, 0,,,0..,1.00). 1.03). As shown in Figure 7 FedCBDR consistently outperforms all baseline methods across incremental tasks, maintaining higher accuracy on both current and previous tasks throughout the training process. Moreover, its performance degrades more slowly as number of tasks increases.
15

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CIFAR10
CIFAR10
80
FedEIGG FedLaf "ARGET
Test Accuracy
Test Accuracy
80
60
60
40
0.5
=1.0
1
Task ID
Task ID
CIFAR100
CIFAR100
CIFAR100
80
60
Accuracy
Test Accuracy
Test Accuracy
60
60
40
40
40
0.1
20
8
0.5
8
110
1
3
2
0
1
2
3
Task ID
Task ID
Task ID
TinylmageNet
TinylmageNet
TinylmageNet
60
60
Test Accuracy
40
Test Accuracy
Test Accuracy
40
40
20
20
20
0.1
B
0.5
=10 B
2
4 Task ID
5
7 9
0
1
3
6 8
9
0
2
4 5
7
9
Task ID
Task ID
Figure 7: Comparison of per-class sample distributions in the replay buffer between FedCBDR and Re-Fed on the CIFAR100 dataset, conducted under a heterogeneity level of B = 0.5, with 10-task split and 5 clients.
A.1.3 Comparison of Per-Class Sample Distributions in the Replay Buffer
We further validate the capability of the proposed FedCEDR to balance per-class sample distributions more complex scenarios. Specifically, we divide the CIFAR100 dataset into 10 tasks. As illustrated in Figure 8 Re-Fed exhibits substantial disparities in the number of replayed samples across classes. For example, in task while classes 10 and contain nearly 100 samples each, class 11 has fewer than 10. In contrast, FedCEDR effectively alleviates such class imbalance, with the number of replayed samples for all classes remaining consistently close to the average (as marked by the red line). This contributes to more stable knowledge retention across tasks and enhances overall model generalization.
A.1.4 Quantitative Analysis of Replay Buffer Size on Test Accuracy
Table Comparison of model performance with varying replay budget M per task across datasets, with the number of clients fixed at 10, and heterogeneity level 3 = 0.5.
CIFARIO
CIFARI00
Methods
M=150
M=300
M=450
M=500
M=1000
M=1500
Re-Fed
39,22
42.93
45.49
28.21
36.40
41.78
FedCBDR
48.15
54.62
59.80
38.34
47.90
51.14
We compare the performance of the data rapedy-based methods, Re-Fed and FedCBDR, under varying replay buffer budgets. Specifically, for CIFAR 10, the buffer size is adjusted among (150, 300, 450). while for CIFAR100, 100, it ranges from (500, 1000, 1500). The number of clients is set to 10, and heterogeneity level 8 0.5. As shown in Table 8, the performance of both methods improves as
16

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Task
Task
Task
100
Average
90
FedCBDR
Number
60
Numbor
75
10
50
20
25
0
Task
4
Task 5
100
75
Numbber
Number
60
50
40
20
20
2
Class Index
Class Index
Class Index
Task
Task 7
Task 8
100
100
80
80
Number
60
Number
60
40
20
20
20
Class
Class
Index
Figure 8: Comparison of per-class sample distributions in the replay buffer between FedCBDR and Re-Fed on the CIFAR100 dataset, conducted under a heterogeneity level of 0.5, with 10-task split and 5 clients.
the buffer size increases, with FedCEDR maintaining clear advantage over Re-Fed under all settings. This also underscores the importance of balancing per-class sample counts in the replay buffer to ensure fair representation and stable performance.
A.1.5 Evaluation on the Impact of Local Training Epochs
To assess the impact of local training intensity, we compare the performance of LANDER, Re-Fed. and FedCBDR, under varying local training epoch settings. Specifically, the evaluation is conducted on CIFARIO divided into tasks and CIFAR 100 divided into tasks, under a federated setting with 10 clients and heterogeneity level of 3 0.5. As shown in Figure 9 both GDR and GDR+TTS consistently outperform the baseline methods (LANDER and Re-Fed) across local training epoch settings on both CIFAR10 and CIFAR100. Moreover, GDR+TTS achieves the highest test accuracy in every configuration. The improvement brought by TTS highlights its necessity in alleviating class imbalance during local training. And, unlike other methods whose performance drops at 10 local epochs due to biased updates, GDR+TTS demonstrates sustained improvement potential.
A.1.6 Performance Assessment of the Final Model Across Tasks
This section compares the final model performance of different methods (LANDER, Re-Fed, FedCBDR) across various tasks. Specifically, all experiments are conducted under federated setting with 5 clients and heterogeneity level of 3 = 0.5. CIFAR10 10 is split into 3 tasks and CIFARIO 100 into 5 tasks. Each task trained for 50 communication rounds, with each client performing local training epochs per round using a batch size of 128. For sample replay, LANDER synthesizes 10,240 samples per task, while Re-Fed and FedCBDR retain 150 and 1,000 real samples per task on CIFAR10 and CIFAR 100, respectively. As shown in Table 9 LANDER suffers from significant forgetting of earlier tasks, as evidenced by its low accuracy only 1.37% on Task severe inability to retain prior knowledge. Moreover, LANDER also shows noticeable decline in performance on the last task, achieving only 57. 57.00% on Task 5 of CIFAR100 100, suggesting that its generalization to new tasks is also limited under non-i.id. conditions. Compared to LANDER and
CIFARIO. This indicates
17

---

CIFAR10
CIFARI00
LANDER Re-Feed G23 GUT--TT
60
Test Accuracy
40
10
Test
LANDER
40-
10
Local Training Epochs
Local Training Epochs
Figure 9: Comparison of the final performance of the LANDER, Re-Fed, GDR, and GDR+TTS methods under different numbers of local training epochs. GDR and TTS are the two functional modules proposed in this work, and GDR+TTS=FedCEDR
Re-Fed, GDR significantly enhances the retention of knowledge from most early tasks. This demonstrates the advantage balanced sample replay over imbalanced sampling. In particular, GDR+TTS outperforms GDR alone, highlighting the effectiveness of the proposed TTS module in mitigating class imbalance and supporting long-term knowledge preservation under non-i.i.d. settings.
Table 9: Per-task and average accuracy (%) of different methods CIFARIO and CIFAR100.
CIFARIO
CIFAR100
Task
Task
Task 3
ALL
Task
Task 2
Task 3
Task
Task 5
ALL
LANDER
1.37
30.00
88.32
44.74
33.95
40.90
43.70
44.45
57.00
44.00
Re-Fed
14.20
18.33
95.88
44.28
23.40
22.00
21.10
34.10
81.70
36.46
GDR
17.07
19.00
96.10
49.26
40.40
37.90
39.25
47.50
80.45
49.10
GDR+TTS
21.43
21.46
96.08
51.30
41.45
38.05
38.50
48.55
81.40
49.59
A.1.7 Scalability, Complexity, and Communication Efficiency
We have conducted additional large-scale experiments by extending the number of clients 50/100 while simulating asynchronous participation, and the client sampling rate set to 0.2. The dataset divided into five tasks, and in Re-Fed and FedCBDR, the number of replay samples set to 150/300 for CIFAR-10 10 and 500/1000 for CIFAR- MAARR-00.TT.. 100.The following results can be summarized:
Table 10: Comparison of continual federated learning methods with 50 clients on CIFAR10 and CIFAR 100 under different Dirichlet data partitions (a).
CIFAR10
CIFAR100
50 Clients
0.5
a = 1.0
0.5
1.0
FedLwF
17.51
19.43
19.99
23.91
LANDER
18.08
21.27
26.96
27.34
Re-Fed (300/500)
29.65
32.45
27.12
28.86
FedCBDR (300/500)
34.76
35.98
28.75
30.90
Re-Fed (600/1000)
36.40
38.46
35.94
37.79
FedCBDR (600/1000)
41.33
45.31
38.54
39.69
18

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Table 11: Computation and communication analysis on CIFARIO and CIFARI00 datasets. Top: computation overhead per client: Bottom: communication cost per round.
Dataset
#Clients Samples/
Feature (K) Client (ng) Dim (d)
ISVD
Feature Extrac- Total
Extra tion Time/Client Time/Client (s)
Time/Client
(s)
CIFARIO 2000
512 512
0.00580 0.00452
0,9083
0,9141
CIFARI00
5
2000
1.0250
1.2952
Dataset
#Clients Samples/
Feature Dim (d)
Upload/Client (MB)
Total Upload per Index Round (MB)
Down-
(K)
Client (nx)
load (KB)
CIFARIO
10
100
512
3.906
19.53
39.06
CIFARIO
10
100
128
0.9765
4.8825
39.06
CIFARIO
10
100
32
0.2441
1.2206
39.06
Morcover, we have further clarified the computational complexity of the SVD step. Client-side ISVD costs dn per client, while server-side SVD costs (NNd²), where N E. ng is the total number of pseudo-features.
We also provide summary of the additional training cost clients, the communication overhead, and the server-side computation incurred by our method in comparison to FedAvg. Overall, the additional cost of performing SVD and computing leverage scores is relatively small.
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26


Reviews:
[{"review_id": "uMBQUPl5go", "reviewer": "Reviewer_3EQU", "summary": "The work is about federated class-incremental learning, which considers class imbalance problem and developed FedCBDR which employs a global coordination mechanism for class-level memory construction and reweights the learning objective. It is composed of two key components, i.e., the global-perspective data replay module and the task-aware temperature scaling module, which respectively achieve balanced replay and alleviate the imbalance issue. The authors also conducted a number of experiments to evaluate its superiority.", "questions": "Refer to the weaknesses.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strength**\n1.  The paper is well-structured with clear organization and motivations. The problem to be addressed is well analyzed and formalized. \n2. The solution achieves both efficiency and efficacy, as demonstrated by the experiments.\n3. Experiments are sufficient;\n4. The authors conduct a wide range of literature reviews, including the latest works.\n\n**Weaknesses** \n\n1. Some symbols are not clearly defined; it seems that there exists an inconsistent use of a single notation.\n2.  Some implementation details are not directly provided, such as the hyperparameters used in the work, the replay buffer size, etc.\n3. Some designs require more detailed explanation. For example, the process of Pseudo Feature Construction uses global model.", "was_revised": true, "final_justification": "The authors' rebuttal and their responses to other reviewers have overall addressed my main concerns. This includes clarifications on certain experimental details and corrections of notational errors. Therefore, I will raise my score accordingly.", "rebuttal": "We sincerely appreciate the professional comments provided by Reviewer 3EQU\n\nW1: Some symbols are not clearly defined; it seems that there exists an inconsistent use of a single notation.\n\nA1: We reviewed all symbol definitions and usages, identified that task indices were inconsistently represented by $i$ and $t$, and have corrected this inconsistency by unifying the notation to $t$.\n\nW2: Some implementation details are not directly provided, such as the hyperparameters used in the work, the replay buffer size, etc.\n\nA2: We have further clarified some experimental details to ensure reproducibility. For example, we specify that LANDER and TARGET generate 10,240 samples per task. Moreover, based on the source code, we report that the momentum of Batch Normalization in LANDER is set to `bn_mmt `= 0.9, the temperature parameter for knowledge distillation is $\\tau = 2$, the temperature coefficient for KL divergence distillation loss is $T = 20.0$, and the number of training steps with synthetic data is `student_train_step` = 50. For Re-Fed, the number of replay samples is kept consistent with our FedCBDR, as referenced in Section~5.1.\n\nW3: Some designs require more detailed explanation. For example, the process of Pseudo Feature Construction uses global model.\n\nA3: The pseudo-feature construction leverages the \\textbf{global model’s feature extractor} to ensure that features from all clients are aligned in a common latent space, which is crucial for meaningful aggregation. Each client first uses the current global model $f_{\\theta}^{(i)} $ to map its local samples into a feature matrix $X_k \\in \\mathbb{R}^{n_k \\times d}$. Then, we perform an ISVD transformation: $X_k = U_k \\Sigma_k V_k^T$, replace $U_k$ with a random orthogonal matrix $P_k$, and generate pseudo-features $\\tilde{X}_k = P_k \\Sigma_k V_k^T$, which are subsequently uploaded to the server. The server concatenates all $\\tilde{X}_k$, applies SVD, and selects representative samples using leverage scores. We will revise Section 4.1 to provide a clearer explanation of this process."}, {"review_id": "pQqzOiiz8g", "reviewer": "Reviewer_scc5", "summary": "This paper focuses on the key challenges in Federated Class-Incremental Learning (FCIL), namely inter-task class imbalance and catastrophic forgetting. The proposed method, FedCBDR, demonstrates a certain level of innovation in its design, particularly in how it achieves class-balanced replay through global reconstruction under the federated setting. Extensive empirical evaluations on multiple benchmark datasets confirm its strong performance, with the method remaining robust even under highly heterogeneous (non-IID) conditions. Overall, this work provides a practical and effective solution framework for FCIL tasks and shows strong potential to serve as a baseline for future research.", "questions": "1. What are the experimental settings for TARGET and LANDER, particularly regarding the number of generated samples?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper is well-motivated and tackles a critical problem in Federated Class-Incremental Learning (FCIL), namely the challenge of class imbalance and catastrophic forgetting across tasks.\n2. The proposed FedCBDR method is technically sound and thoughtfully designed. It integrates global feature reconstruction and task-aware temperature scaling in a lightweight and modular manner.\n3. The experimental evaluation is thorough and convincing. It includes multiple datasets, diverse settings, ablation studies, and analysis that clearly demonstrate the effectiveness of each component of the method.\n\n\nMajor Weaknesses\n1. The feature uploading process appears to introduce additional communication overhead.\n2. The authors claim in line 199 that training a generator incurs higher costs than local training. Is there any empirical evidence provided to support this statement?\n\n\nMinor Weaknesses\n1. The bolded content mentioned in the appendix appears to be missing in the actual tables.\n2. The notation used in Figure 2 appears to be inconsistent with that in the main text.\n3. The meaning of Concat() in Equations (7) and (9) has not been clearly defined.", "was_revised": true, "final_justification": "The respose addressed my concens, and I will keep my score.", "rebuttal": "We sincerely appreciate the professional comments provided by Reviewer scc5\n\nW1: The feature uploading process appears to introduce additional communication overhead.\n\nA1: The additional communication overhead primarily comes from uploading the pseudo-features \n($\\tilde{X}_k \\in \\mathbb{R}^{n_k \\times d}$) generated by each client during the GDR module. \nThe exact size of this overhead, including per-client and total communication per round, \nis provided in the response to Reviewer i4C7's W6. To further reduce this cost, we can employ feature compression techniques (e.g., projecting features from 512 to 128 dimensions, reducing data by 75\\%) and client sampling strategies, where only a subset of clients upload pseudo-features in each round. \n\nW2: The authors claim in line 199 that training a generator incurs higher costs than local training. \n\nA2: For the LANDER method, each round of local training on a client requires 15.6657 seconds and 1582 MB of memory, whereas the sample generation process takes 753.9422 seconds and 2430 MB of memory.  \n\nW3: The bolded content mentioned in the appendix appears to be missing in the actual tables.\n\nA3: We will address this issue in the revised manuscript by **highlighting the best performance values in the tables using boldface** for better clarity.\n\nW4: The notation used in Figure 2 appears to be inconsistent with that in the main text.\n\nA4: We reviewed the symbols in Figure 2 and the corresponding text, and found an inconsistency in the task sequence notation (using both $t$ and $i$). We have corrected this issue and unified the notation by using $t$ to represent the task index.\n\nW5: The meaning of Concat() in Equations (7) and (9) has not been clearly defined.\n\nA5: In Equations (7) and (9), **Concat()** denotes the **row-wise concatenation of pseudo-features** from all participating clients into a single matrix. Specifically, if client $k$ provides pseudo-features $\\tilde{X}_k \\in \\mathbb{R}^{n_k \\times d}$, then:\n\n$\\text{Concat}(\\tilde{X}_1, \\tilde{X}_2, \\ldots, \\tilde{X}_K) =\n\\begin{bmatrix}\n\\tilde{X}_1 \\\\\\\\\n\\tilde{X}_2 \\\\\\\\\n\\vdots \\\\\\\\\n\\tilde{X}_K\n\\end{bmatrix} \\in \\mathbb{R}^{N \\times d},$\n\nwhere $N = \\sum_k n_k$. We have added this definition and explanation to Section 4.1 in the revised manuscript."}, {"review_id": "CFSHr9mcYB", "reviewer": "Reviewer_Csv6", "summary": "This paper proposes FedCBDR, a class-wise balancing data replay strategy for Federated Class-Incremental Learning (FCIL). To alleviate the replay-induced class imbalance caused by local client views and task dynamics, FedCBDR introduces two key modules: Global-perspective Data Replay (GDR) and Task-aware Temperature Scaling (TTS). GDR constructs a privacy-preserving global pseudo-feature space to enable representative and balanced sample selection. TTS dynamically calibrates softmax temperature and loss weighting based on task recency to mitigate model overconfidence in majority classes. Extensive experiments demonstrate that FedCBDR consistently outperforms existing methods across multiple datasets.", "questions": "i. Will uploading features pose a risk of privacy leakage?\n\nii. How can the additional communication cost caused by uploading features be reduced? \n\niii. Is the performance of the proposed method sensitive to the order in which tasks are presented?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\ni. The proposed method is both simple and effective, with a technically sound overall design. This work has potential to become a new baseline in federated class-incremental learning (FCIL).\n\nii. The overall flow of the paper is sufficiently clear, and the structure of the manuscript is well-organized.\n\niii. The authors conducted extensive experiments and validated that the proposed FedCBDR outperforms existing methods. They also provided case studies to further illustrate the effectiveness of the approach.\n\nWeaknesses\n\ni. The meaning of n_k^i in lines 136 and 139 may need further clarification. Is it related to defined in line 138? Some descriptions may need correction. For instance, the term “Global-perspective Active Data Replay” in line 208 should be revised to “Global-perspective Data Replay”\n\nii. It is recommended to unify the notation throughout the paper. For example, in Section 3, t is used to denote tasks, while in Section 4, i is used for the same purpose. \n\niii. The paper could better explain the motivation for using leverage scores and how it compares to random or uniform sampling strategies.", "was_revised": true, "final_justification": "The rebuttal has adequately addressed my concerns. Please ensure that the additional experimental results and discussions provided in the rebuttal are incorporated into the final version to enhance completeness and clarity.", "rebuttal": "We sincerely appreciate the professional comments provided by Reviewer Csv6\n\nW1: The meaning of n_k^i in lines 136 and 139 may need further clarification. Is it related to defined in line 138? Some descriptions may need correction. For instance, the term “Global-perspective Active Data Replay” in line 208 should be revised to “Global-perspective Data Replay”\n\nA1: $n_k^i$ denotes the number of samples of client $k$ in the $i$-th task. And we have also corrected the full name of the GDR module to ``Global-perspective Data Replay.'\n\nW2: It is recommended to unify the notation throughout the paper. For example, in Section 3, t is used to denote tasks, while in Section 4, i is used for the same purpose.\n\nA2: Upon reviewing all symbol definitions and usages, we have standardized the task notation to $t$.\n\nW3: The paper could better explain the motivation for using leverage scores and how it compares to random or uniform sampling strategies.\n\nA3: Compared to random or uniform sampling, which ignores data geometry and may select redundant or less informative samples, leverage-score-based sampling prioritizes samples that span the critical directions of variation. This leads to a more balanced and informative replay buffer, particularly under class-imbalance scenarios. And we have compared our method with uniform sampling in the response to Reviewer i4C7's W2, where the results consistently demonstrate the superiority of our approach due to its ability to select more representative and informative samples.\n\nW4: Will uploading features pose a risk of privacy leakage?\n\nA4: Our method does not upload raw data but instead transmits pseudo-features $\\tilde{X}_k = P_k \\Sigma_k V_k^{\\top}$, which are protected by random orthogonal transformations, where $P_k$ is generated locally on the client and kept secret. Given any $\\tilde{X}_k$, there exist infinitely many pairs $(X_k', P_k')$ satisfying the same decomposition, making it impossible to uniquely reconstruct the original samples. This random rotation mechanism functions as a randomized privacy protection approach.\n\nW5: How can the additional communication cost caused by uploading features be reduced?\n\nA5: To reduce the communication overhead caused by feature uploading, we combine feature dimension reduction with sparsification and quantization. Specifically, PCA, random projection, or bottleneck layers can be used to map high-dimensional features (e.g., $d = 512$) to a lower-dimensional space (e.g., $d = 64$), reducing the transmitted data volume by more than 80\\% with minimal impact on model performance. In addition, applying sparsification to retain only the most informative elements of each feature vector, or using 8-bit/16-bit quantization instead of floating-point encoding, can further compress the communication cost.\n\nW6: Is the performance of the proposed method sensitive to the order in which tasks are presented?\n\nA6: The order of tasks is implicitly determined by the random seed, and changing the seed will alter both data splits and task order. To evaluate robustness, we report the mean and standard deviation across multiple random seeds in Tables 1, 2, 6, and 7. The results show that our method consistently achieves better performance in both mean accuracy and variance compared to baselines, indicating that FedCBDR is not sensitive to task order."}, {"review_id": "tBbqJVo6ES", "reviewer": "Reviewer_i4C7", "summary": "This paper proposes FedCBDR, a new method to address catastrophic forgetting in federated class-incremental learning (FCIL) by explicitly tackling class imbalance issues. The authors identify two sources of imbalance in replay-based FCIL: (i) imbalance within the replay buffer due to clients selecting examples without global awareness, and (ii) imbalance between replayed old classes and newly introduced classes in incremental tasks. To mitigate these, FedCBDR introduces two key components. First, a global-perspective data replay (GDR) module constructs a pseudo global representation of prior tasks in a privacy-preserving way and uses it to guide class-balanced exemplar selection across clients. Second, a task-aware temperature scaling (TTS) module dynamically calibrates prediction logits (with task-specific temperature adjustment and instance-level weighting) to reduce overconfidence in majority classes while boosting minority classes. The method allows each client to incorporate a globally informed, class-balanced replay selection and applies confidence scaling to handle long-tail class distributions over time. Experimental results show that FedCBDR yields significantly improved retention of old classes and overall accuracy compared to existing methods. Evaluations on three benchmark image classification datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet) under various non-i.i.d. settings demonstrate 2%–15% higher Top-1 accuracy than six state-of-the-art FCIL approaches.", "questions": "#1. Pseudo-Feature Construction: The mechanism for constructing and using pseudo global features in the GDR module could be described more clearly. Currently, it's mentioned that each client performs an inverse SVD (ISVD) on local features to produce pseudo-features, and then the server aggregates and applies SVD to select representative samples. This is a complex procedure – for instance, what exactly are the \"pseudo-features\" mathematically, and how is inverse SVD used? It would help if the paper included a concise algorithm or a toy example illustrating this process step-by-step. In particular, formally and rigorously analyzing how privacy is preserved (e.g. are the random orthogonal bases used in ISVD shared or kept secret? Can the server reconstruct original data from the pseudo-features?) is essential.\n\n#2. Comparison to GLFC or Related Methods: As noted in the weaknesses, GLFC is a closely related prior work tackling a similar problem. We invite the authors to clarify how FedCBDR differs from and improves upon GLFC (and any other prior FCIL methods with global coordination). Is the key difference that FedCBDR achieves true class-balanced replay, whereas GLFC focuses on gradient alignment? If so, emphasizing this distinction in the introduction or related work would sharpen the paper's contribution. Adding a direct experimental comparison would be necessary.\n\n#3. TTS Hyperparameter Sensitivity & Selection: The Task-aware Temperature Scaling (TTS) module's performance is shown to be sensitive to the choice of its hyperparameters: τ_old,τ_new,w_old,w_new (as indicated in Figure 4 ). (i) The paper provides specific tuning ranges used in the experiments (e.g., τ_old∈{0.8,0.9}). How were these particular ranges determined? Was it based on prior work, preliminary experiments, or other heuristics? (ii) Is there a more principled approach or a robust heuristic for setting these hyperparameters that goes beyond dataset-specific grid search? For instance, could these parameters be adapted based on observable task statistics, such as class imbalance ratios or model confidence levels on old versus new tasks?\n\n#4. Computational Cost & Scalability of GDR: The GDR module involves client-side ISVD and server-side SVD operations, as well as communication of features and scores. What is the theoretical computational complexity of these operations? Furthermore, how much data (in terms of volume, e.g., bytes) needs to be communicated per round or per task between the clients and the server (e.g., for pseudo-features, leverage scores, selected sample indices)? Crucially, how do these computational and communication costs scale with key parameters such as the number of clients (K), the number of samples per client, the dimensionality of the features (d), and the total number of tasks? We invite the authors to provide a theoretical complexity analysis for the main computational steps in GDR. Additionally, providing empirical measurements (e.g., wall-clock time for computations, actual data volume transmitted) from the experimental setup would be highly beneficial.\n\n#5. Realistic Federated Settings: The authors are encouraged to discuss how FedCBDR might extend to more realistic FL scenarios. For example, what if the number of clients is very large or if clients drop in and out (asynchronous participation)? Does the method require any synchronization (e.g. all clients must send features at the end of each task) that could be problematic if some clients are slow or offline?", "limitations": "**Author-Acknowledged Limitations:**\n\n#1. Feature Transmission Costs in FedCBDR: The authors state an intention to \"investigate lightweight sampling strategies to reduce feature transmission costs in FedCBDR\". This implicitly acknowledges that the current GDR module, which involves clients sending transformed features $X_k^{(i)'}$ to the server, may incur significant communication overhead. To better motivate this future work and provide context, it would be beneficial to quantify the current feature transmission costs.\n\n#2. Hyperparameter Sensitivity of Balancing Methods (TTS): The plan to \"develop more robust post-sampling balancing methods that mitigate class imbalance with less sensitivity to hyperparameters\"  directly addresses the observation that the TTS module's effectiveness can depend on careful tuning (Figure 4). \n\n#3. Sampling for Globally Imbalanced Distributions: The authors identify that \"designing sampling strategies for globally imbalanced distributions remains an open problem\". It would be helpful to clarify how this \"globally imbalanced distribution\" problem differs from the class imbalance issues that FedCBDR currently aims to tackle (i.e., imbalance within replay buffers and between replayed and new task samples). If this refers to a more extreme or persistent form of imbalance that affects all tasks and clients from the outset, it would be useful to frame the scope of the current work more precisely against this larger, acknowledged challenge.\n\n**Missing or Under-Discussed Limitations:**\n\n#1. Depth of Privacy Guarantees for GDR: The assertion that GDR is \"privacy-preserving\" is not sufficiently substantiated with rigorous analysis. This stands as a critical limitation for a method proposed for federated learning, where privacy is a cornerstone. The authors should undertake a formal privacy analysis of the GDR module (e.g., within the differential privacy framework or by analyzing resistance to specific attack models). \n\n#2. Server-Side Computational Bottleneck for GDR: The SVD operation performed on the aggregated pseudo-feature matrix $X_k^{(i)'}$ on the server can become a computational bottleneck, particularly as the number of participating clients, the number of samples per client, or the dimensionality of the features increases. This potential limitation should be explicitly discussed. The authors could suggest potential mitigation strategies for future work, such as the use of approximate SVD methods, incremental SVD, or randomized SVD techniques that might offer a better trade-off between accuracy and computational cost. Defining the scale (in terms of number of clients, total samples, feature dimensions) at which FedCBDR is currently practical based on their experimental setup would also be informative.\n\n#3. Generality of Datasets: The experiments are focused on image classification benchmarks with relatively small images. It remains to be seen if the approach would scale to more complex data (e.g. high-resolution images like ImageNet-1k). We invite the authors to evaluate FedCBDR on ImageNet-1k dataset.\n\n#4. Assumption of Synchronous Communication: The training pipeline described in Algorithm 1 implies a synchronous federated learning setup, where the server waits for updates (local models and pseudo-features) from all participating clients before proceeding with aggregation and the SVD operation. Real-world federated learning systems often have to contend with asynchronous client participation, device dropouts, or stragglers. We invite the authors to discuss whether the FedCBDR framework can be adapted to asynchronous settings or acknowledge the reliance on synchronous communication as a current limitation.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Quality Strengths:**\n\nThe technical approach of FedCBDR appears valid. The two primary components, GDR and TTS, are motivated in the context of the class imbalance problem prevalent in FCIL. While the application of SVD for identifying globally salient features and the use of temperature scaling for calibrating the learning objective is well-established machine learning techniques, they are in this paper combined and applied to the specific challenges of the FCIL setting. \n\n**Quality Weaknesses:** \n\n#1. The claim that the GDR module is \"privacy-preserving\" is not formally substantiated with a rigorous privacy analysis. While raw data samples are not directly shared with the server, the ISVD process involves a client-specific matrix $P_k^{(i)}$ and a globally shared matrix $Q^{(i)}$ in the transformation $X_k^{(i)'}=P_k^{(i)} X_k^{(i)} Q^{(i)}$. The subsequent aggregation of these transformed features $X_k^{(i)'}$ at the server, followed by SVD, might still be vulnerable to various inference attacks or information leakage about the original client data. The paper cites references in the context of SVD, which pertain to \"efficient decentralized federated singular vector decomposition\" and \"practical lossless federated singular vector decomposition\" respectively. However, the specific privacy properties of these methods and a clear explanation of how they apply to the FedCBDR framework to ensure privacy are not adequately detailed. We invite the authors to present a formal and rigorous data privacy analysis (e.g., differential privacy guarantees). \n\n#2. For the experiments, the absence of GLFC (Global-Local Forgetting Compensation, NeurIPS 2023) or its contemporary equivalents is critical, though they are discussed in related work. Given that GLFC specifically addresses forgetting in FCIL with a form of global coordination, it would be a natural baseline to include. We invite the authors to present a comparison with it. Similarly, one might wonder about simple baselines like FedAvg + replay buffer with uniform sampling (without FedCBDR's balancing) as a point of reference – although the paper does compare to Re-Fed, which is a strong replay-based method, an even simpler baseline could show the raw effect of class imbalance.\n\n**Clarity Strengths:**\n\nIn general, the paper is well-written and structured. The problem formulation and related work give a clear background on FCIL and existing replay strategies. The method is illustrated with a framework diagram (Figure 2) and detailed step-by-step description, which aids understanding of the GDR and TTS modules.\n\n**Clarity Weaknesses:**\n\n#1. The description of the SVD/ISVD process within the GDR module could be more precise. Labeling the transformation $X_k^{(i)'}=P_k^{(i)} X_k^{(i)} Q^{(i)}$ as an \"encryption\" might be misleading if not used in a formal cryptographic sense. Further clarification on this transformation (e.g., feature obfuscation or rotation for privacy) and more details on the generation, properties (e.g., orthogonality), and secure sharing mechanism for the matrices $P_k^{(i)}$ and $Q^{(i)}$ are necessary. \n\n#2. In addition, we invite the authors to clarify the dimensions of the matrix $\\sum^{(i)'}$ resulting from the SVD operation. \n\n#3. Besides, there appears to be a mismatch or mislabeling concerning Figure 7 and Figure 8 in the appendix. The captions for these two figures appear to be identical, while the figures themselves depict different types of information (performance curves versus sample distributions).\n\n**Significance Strengths:**\n\nThe paper addresses a challenge in federated class-incremental learning – the class imbalance in replay data – which is often overlooked in prior work. By articulating the issue (both local replay bias and long-tailed new vs. old class distribution), the authors establish a motivation for FedCBDR. The proposed solution directly targets this gap.\n\n**Significance Weaknesses:**\n \n#1. Scalability concerns may limit the perceived significance of large-scale, real-world FL deployments. The experiments are conducted with a relatively small number of clients ($K=5$ in the main paper, $K=10$ in the appendix). The performance, computational feasibility (especially the server-side SVD in GDR), and communication efficiency of FedCBDR on much larger federated networks, potentially involving hundreds or thousands of clients, are not demonstrated.\n\n#2. The sensitivity of the TTS module to its hyperparameters ($\\tau_{old}, \\tau_{new}, w_{old}, w_{new}$) is a practical concern. Figure 4 illustrates that performance can vary considerably with different hyperparameter settings, and inappropriate choices can be detrimental. If extensive and dataset-specific tuning is required for these hyperparameters in each new scenario, it reduces the method's ease of use and practical significance. Indeed, the authors also acknowledge this by proposing future work to develop methods with \"less sensitivity to hyperparameters\".\n\n**Originality Strengths:**\n\nThe idea of incorporating a global perspective into replay buffer construction seems to be new in the FCIL context. Instead of each client selecting exemplars myopically, FedCBDR's GDR module uses a server-side aggregation of pseudo-features (via SVD-based feature decomposition) to guide class-balanced sampling across clients. This mechanism appears to be a novel combination of techniques (singular value decomposition for representative selection, leverage score sampling, etc.) not seen in prior FCIL methods. Similarly, the TTS module introduces a multi-level logit calibration scheme for federated learning, which seems to be an original way to handle class imbalance between tasks.\n\n**Originality Weaknesses:**\n \n#1. The method builds upon known techniques – exemplar replay and temperature scaling – by combining them with a global feature aggregation step. Some elements have precedents: for example, prior work GLFC (referenced as [9]) also introduced global coordination (via gradient compensation and prototype sharing) to address forgetting. \n\n#2. FedCBDR's global pseudo-feature approach is different, but the authors could better clarify how their method fundamentally advances beyond such earlier approaches. \n\n#3. The temperature scaling idea for class imbalance, while effective, is a relatively straightforward adaptation of calibration methods to this setting.", "was_revised": true, "final_justification": "The authors answered most of my questions through two rounds of rebuttal. I am happy to raise my score to 4. Borderline Accept.", "rebuttal": "We sincerely appreciate the professional comments provided by Reviewer i4C7\n\nW1: Privacy analysis\n\nA1: For any feature vector $x \\in \\mathbb{R}^d$, a random orthogonal matrix $P \\in \\mathbb{R}^{d \\times d}$ satisfies:\n$$\nP^T P = P P^T = I.\n$$\nHence, the orthogonal transformation $x' = P x$ preserves the L2-norm of the vector:\n$$\n\\|x'\\|_2 = \\|P x\\|_2 = \\sqrt{x^{T} P^{T} P x} = \\|x\\|_2.\n$$\nThis implies that all points on the unit sphere are merely rotated by $P$, with their original coordinate meaning destroyed. $P$ is sampled uniformly from the Haar distribution on $O(d)$, then $y = P x$ is effectively the representation of $x$ under an **unknown random rotation basis**.  \nWithout knowledge of $P$, an attacker observing $y$ cannot deduce the unique $x$, because:\n$$\n\\forall x' \\text{ with } \\|x'\\|_2 = \\|x\\|_2, \\ \\exists P' \\in O(d), \\ y = P' x'.\n$$\nIn other words, infinitely many $x'$ on the sphere could lead to the same $y$ with an appropriate rotation $P'$.\n\nW2: Experiments with GLFC and the simple replay baseline\n\nA2: We conducted 5-task class-incremental experiments under different data heterogeneity settings and 5 clients. As shown in the Table, FedCBDR achieves the best performance across all cases.\n\n| Method           | CIFAR10 (α=0.5) | CIFAR10 (α=1.0) | CIFAR100 (α=0.5) | CIFAR100 (α=1.0) |\n|------------------|-----------------|-----------------|------------------|------------------|\n| **GLFC**         | 50.34 ± 2.8     | 60.21 ± 3.1     | 36.72 ± 2.5      | 44.69 ± 1.8      |\n| **FedAvg+Uniform** | 46.49 ± 4.2     | 55.57 ± 3.5     | 33.41 ± 3.6      | 44.73 ± 2.9      |\n| **Re-Fed**       | 53.46 ± 3.5     | 60.73 ± 4.3     | 38.42 ± 2.9      | 45.28 ± 2.6      |\n| **FedCBDR**      | **64.11 ± 1.2** | **65.20 ± 1.9** | **49.76 ± 2.7**  | **52.06 ± 1.5**  |\n\nW3: SVD/ISVD Transformation and Privacy\n\nA3: We replaced the term “encryption” with “orthogonal obfuscation,” and clearly defined the transformation:\n   $$\n   X_k^{(i)'} = P_k^{(i)} X_k^{(i)} Q^{(i)},\n   $$\n   where $P_k^{(i)}$ and $Q^{(i)}$ are orthogonal matrices preserving norms but rotating the feature space. And $P_k^{(i)}$ is generated locally on each client (via QR decomposition of random Gaussian matrices) and never transmitted to the server. The global $Q^{(i)}$ is initialized by the server and publicly shared but does not leak sensitive information.\n\nW4: Dimensions of $\\Sigma^{(i)'}$\n\nA4: In the original manuscript, we wrote the full SVD as:\n$$\nX^{(i)'} = U^{(i)'} \\Sigma^{(i)'} V^{(i)'T},\n$$\nwith $U^{(i)'} \\in \\mathbb{R}^{n \\times n}$, $\\Sigma^{(i)'} \\in \\mathbb{R}^{n \\times d}$, \nand $V^{(i)'} \\in \\mathbb{R}^{d \\times d}$. While this is correct for the full SVD, in practice we only need the reduced SVD form with\n$$\nU^{(i)'} \\in \\mathbb{R}^{n \\times r}, \\ \n\\Sigma^{(i)'} \\in \\mathbb{R}^{r \\times r}, \\ \nV^{(i)'} \\in \\mathbb{R}^{d \\times r},\n$$\nwhere $r = \\min(n, d)$. We will revise Section 4.1 to clarify this notation and explicitly define $r$ as the effective rank used for pseudo-feature construction.\n\nW5: Figure captions\n\nA5: We have revised the caption of Figure 7 to: “Performance comparison of all methods across varying settings on CIFAR10 (3 tasks, $\\beta=\\{0.1,0.5\\}$), CIFAR100 (5 tasks, $\\beta=\\{0.1,0.5,1.0\\}$), and TinyImageNet (10 tasks, $\\beta=\\{0.1,0.5,1.0\\}$).”\n\nW6: Scalability, Complexity, and Communication Efficiency concerns\n\nA6: We have conducted additional large-scale experiments by extending the number of clients to 50/100 while simulating asynchronous participation, and the client sampling rate is set to 0.2. The dataset is divided into five tasks, and in Re-Fed and FedCBDR, the number of replay samples is set to 150/300 for CIFAR-10 and 500/1000 for CIFAR-100.The following results can be summarized: \n| 50 Clients            | CIFAR10 (alpha=0.5) | CIFAR10 (alpha=1.0) | CIFAR100 (alpha=0.5) | CIFAR100 (alpha=1.0) |\n|-----------------------|---------------------|----------------------|-----------------------|-----------------------|\n| FedLwf                | 17.51               | 19.43                | 19.99                 | 23.91                 |\n| LANDER                | 18.08               | 21.27                | 26.96                 | 27.34                 |\n| Re-Fed (300/500)      | 29.65               | 32.45                | 27.12                 | 28.86                 |\n| FedCBDR (300/500)     | 34.76               | 35.98                | 28.75                 | 30.90                 |\n| Re-Fed (600/1000)     | 36.40               | 38.46                | 35.94                 | 37.79                 |\n| FedCBDR (600/1000)    | 41.33               | 45.31                | 38.54                 | 39.69                 |\n\nMoreover, we have further clarified the computational complexity of the SVD step,\nClient-side ISVD: $𝑂(𝑛_𝑘𝑑^2)$ per client.\nServer-side SVD: $𝑂(𝑁𝑑^2)$, where $𝑁=\\sum_kn_k$ is the total number of pseudo-features.\n\nAnd we provide a summary of the additional training cost on clients, communication overhead, and computation cost on the server incurred by our proposed method in comparison to FedAvg. **Overall, the additional cost of performing SVD and computing leverage scores is relatively small.**\n\n| Dataset   | #Clients (K) | Samples/Client (n_k) | Feature Dim (d) | ISVD Time/Client (s) | Feature Extraction Time/Client (s) | Total Extra Time /Client (s) |\n|-----------|--------------|----------------------|-----------------|-----------------------|-------------------------------------|-----------------------|\n| CIFAR10   | 5            | 2000                 | 512             | 0.00580                  | 0.9083                                 | 0.9141                  |\n| CIFAR100  | 5            | 2000                 | 512             | 0.00452                  | 1.025                                 | 1.2952                  |\n\n| Dataset   | #Clients (K) | Samples/Client (n_k) | Feature Dim (d) | Upload/Client (MB) | Total Upload per Round (MB) | Index Download (KB) |\n|-----------|--------------|----------------------|-----------------|--------------------|-----------------------------|---------------------|\n| CIFAR10   | 10           | 100                  | 512             | 3.906               | 19.53                        | 39.06                  |\n| CIFAR10   | 10           | 100                  | 128             | 0.9765               | 4.8825                         | 39.06                  |\n| CIFAR10   | 10           | 100                  | 32              | 0.2441              | 1.2206                        | 39.06                 |\n\nThe table shows that the communication cost scales linearly with the feature dimension $d$ and the number of clients $K$, meaning that higher feature dimensions or more clients lead to proportionally increased upload costs.\n\n| Dataset   | Total Samples (N) | Feature Dim (d) | Server SVD Time (s) |\n|-----------|-------------------|-----------------|----------------------|\n| CIFAR10   | 1000              | 512             | 0.3066               |\n| CIFAR100  | 1000              | 512             | 0.3362               |\n\nW7: TTS Hyperparameter Sensitivity\n\nA7: (i) The temperature parameters in the TTS module are chosen based on empirical insights and grid search. A lower temperature for old classes ($ \\tau_{\\mathrm{old}} < 1 $) sharpens logits to reduce forgetting, while a higher temperature for new classes ($ \\tau_{\\mathrm{new}} > 1 $) smooths logits to avoid overfitting to new classes. (ii) Alternatively, the temperatures and weights ($ \\tau_{\\mathrm{old}}, \\tau_{\\mathrm{new}}, w_{\\mathrm{old}}, w_{\\mathrm{new}} $) can be treated as **learnable parameters**, optimized during training, similar to recent approaches like *FedETF* [Li et al., ICCV 2023].\n\nW8: Compare with GLFC\n\nA8: Unlike GLFC, which relies on gradient compensation and prototype sharing for global coordination, FedCBDR uses SVD-based pseudo-feature construction and leverage score sampling to build a class-balanced replay buffer. Moreover, the TTS module dynamically assigns temperatures ($\\tau_{\\mathrm{old}} < 1, \\tau_{\\mathrm{new}} > 1$) to mitigate prediction bias. Experiments on CIFAR10 and CIFAR100 show that FedCBDR significantly outperforms GLFC, demonstrating its effectiveness.\n\nW9: Temperature Scaling\n\nA9: Compared to existing temperature scaling, our TTS module is specifically designed to address the imbalance between old and new classes in incremental learning. By applying different temperatures (\\$\\tau\\_{\\mathrm{old}} < 1, \\tau\\_{\\mathrm{new}} > 1\\$) and weights (\\$w\\_{\\mathrm{old}}, w\\_{\\mathrm{new}}\\$), TTS balances predictions and mitigates new-class overconfidence. Ablation studies confirm its crucial role, as removing TTS significantly degrades performance.\n\nW10:  Pseudo-Feature Construction\n\nA10: For each client's local feature matrix $X_k \\in \\mathbb{R}^{n_k \\times d}$, we perform SVD $X_k = U_k \\Sigma_k V_k^{\\top}$ and replace $U_k$ with a random orthogonal matrix $P_k$ to generate pseudo-features $\\tilde{X}_k = P_k \\Sigma_k V_k^{\\top}$, which perturbs the local feature space while preserving spectral information. The server aggregates $\\tilde{X}_k$ and applies SVD with leverage scores to select representative samples for constructing a class-balanced replay buffer. For privacy, $P_k$ remains local, and its randomness ensures that $X_k$ cannot be reconstructed from $\\tilde{X}_k$.\n\nW11: Generality of Datasets\n\nA11: Our experiments are conducted on CIFAR10, CIFAR100, and TinyImageNet, which are widely used FCIL benchmarks and also adopted by methods such as GLFC, LANDER, and Re-Fed to ensure fair comparisons. The core modules of FedCBDR, GDR and TTS, are resolution-agnostic and can, in principle, be applied to larger datasets like ImageNet-1k, with feature dimensionality reduction helping to lower computation and communication costs. Due to time limitations, we plan to extend FedCBDR to such large-scale benchmarks in future work."}]

Author rebuttal:

Meta-review: This paper presents a timely and impactful contribution that bridges federated learning and class-incremental learning, offering a solution that is both technically sound and practically relevant. In particular, it makes the first attempt to realize a global-perspective solution for class-balanced data replay in federated class-incremental learning, a novelty that was specifically noted and appreciated by the reviewers. The authors have convincingly addressed reviewers’ concerns during the rebuttal, further strengthening the clarity, reproducibility, and scalability of the work. Given its methodological innovation, comprehensive experimental validation, and broad relevance across multiple research communities, the paper is expected to stimulate significant interest and discussion. Overall, it stands out as a high-quality submission well-suited for an oral presentation.
Decision: Accept (oral)