C$^2$Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
Authors: Kunlun Xu
yibo feng
Jiangmeng Li
Yongsheng Qi
Jiahuan Zhou
Venue: NeurIPS 2025 poster ()
Keywords: Continual Learning; Federated learning
Primary area: applications

Abstract: Federated continual learning (FCL) tackles scenarios of learning from continuously emerging task data across distributed clients, where the key challenge lies in addressing both temporal forgetting over time and spatial forgetting simultaneously. Recently, prompt-based FCL methods have shown advanced performance through task-wise prompt communication.
In this study, we underscore that the existing prompt-based FCL methods are prone to class-wise knowledge coherence between prompts across clients. The class-wise knowledge coherence includes two aspects: (1) intra-class distribution gap across clients, which degrades the learned semantics across prompts, (2) inter-prompt class-wise relevance, which highlights cross-class knowledge confusion. During prompt communication, insufficient class-wise coherence exacerbates knowledge conflicts among new prompts and induces interference with old prompts, intensifying both spatial and temporal forgetting. To address these issues, we propose a novel Class-aware Client Knowledge Interaction (C$^2$Prompt) method that explicitly enhances class-wise knowledge coherence during prompt communication. Specifically, a local class distribution compensation mechanism (LCDC) is introduced to reduce intra-class distribution disparities across clients, thereby reinforcing intra-class knowledge consistency. Additionally, a class-aware prompt aggregation scheme (CPA) is designed to alleviate inter-class knowledge confusion by selectively strengthening class-relevant knowledge aggregation. Extensive experiments on multiple FCL benchmarks demonstrate that C$^2$Prompt achieves state-of-the-art performance. Our code will be released.

C Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
Kunlun Xu*
Yibo Feng
Wangxuan Institute of Computer Technology Peking University
Wangxuan Institute of Computer Technology
Peking University
Beijing, China
Beijing. China
xkl@stu. pku. edu.
20220909170120stdd ueste. edu.
Jiangmeng Li
Yongsheng Qi
University of Chinese Academy of Sciences Beijing, China
Inner Mongolia University of Technology Hohhot, Inner Mongolia Autonomous Region qys@imut. edu, cn
j iangmeng20190iscas. ac cn
Jiahuan Zhou
Wangxuan Institute Computer Technology Peking University
Beijing, China
j jiahuanzhouOpk. wdw.::zhhhoupuueeuucc.c.... edu.
Abstract
Federated continual learning (FCL) tackles scenarios of learning from continuously emerging task data across distributed clients, where the key challenge lies in addressing both temporal forgetting over time and spatial forgetting simultaneously. Recently, prompt-based FCL methods have shown advanced performance through task-wise prompt communication. In this study, we underscore that the existing prompt-based FCL methods are prone to class- wise knowledge coherence between prompts across clients. The class-wise knowledge coherence includes two aspects: (1) intra-class distribution gap across clients, which degrades the learned semantics across prompts, (2) inter-prompt class-wise relevance, which highlights cross- class knowledge confusion. During prompt communication, insufficient class- wise coherence exacerbates knowledge conflicts among prompts and induces interference with old prompts, intensifying both spatial and temporal forgetting. To address these issues, propose novel Class-aware Client Knowledge Interaction (C2 ("Pommpt) method that explicitly enhances class-wise knowledge coherence during prompt communication. Specifically, a local class distribution compensation mechanism (LCDC) is introduced to reduce intra-class distribution disparities across clients, thereby reinforeing intra-class knowledge consistency. Additionally, a class-aware prompt aggregation scheme (CPA) is designed to alleviate inter- class knowledge confusion by selectively strengthening class-relevant knowledge aggregation. Extensive experiments multiple FCL benchmarks demonstrate that C² Prrrompt achieves state-of-the the-art performance. Our source code is available https://gihuub.com//ojjjjj [99] NNNNuuPP22225-22PPomm
'Equal contribution Cooresponning author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
With the proliferation of edge computing and IoT devices [. federated continual learning (FCL) has emerged critical paradigm for enabling intelligent systems continuously learn from decentralized data streams while preserving data privacy 3 3 However, this setting presents dual challenge: models must overcome catastrophic forgetting across sequential tasks (temporal dimension) while adapting to heterogeneous data distributions among clients (spatial dimension) 6
While traditional continual learning methods (8 9 [2 and federated learning approaches I3
14 15
16 [7 have made significant progress independently, their combined formulation in FCL
struggles to address the superimposed forgetting effectively 66 T8 19
Existing FCL methods predominantly address the challenges of spatio- temporal knowledge transfer through data synthesis and parameter regular- ization (20). However, data synthesis approaches 21 22]
Stage
Prommje-class
Histogram
Rg
Server
lient
Pronupi
Procup:
typically depend on deep generative models trained on raw data, raising concerns regarding data privacy. In contrast, parameter regularization methods (3] attempt to balance learning and forgetting but often suffer from limited capacity to acquire new knowledge effectively. Recently, prompt-basee learning 19 promis-
Pmee-Prrppt.. Caneriiie Relevtence
Mullipk
(a) Clnss--wise Procapt Relevance
Nirer Llman
Sampiia
Clieni
Old
Client
Cliant
Clisii
Distibbtion Ovedup O,
(b) Influence of Intm-Cllise Distribution Overliap Og
23 24 has emerged ing solution for FCL by maintaining task-specific prompts that store knowl- edge representations while leveraging frozen pre-trained model [25] 266..
Figure I: In FCL, class-wise knowledge coherence includes two aspects: (a) inter-prompt class-wise relevance which influences prompt aggregation in the server, (b) intra-class distribution gap (overlap) across clients which influences the locally learned semantics of each class.
To overcome the overfitting to local distribution, some methods introduce inter-client prompt communication to
improve the robustness [19]. Despite their potential, these approaches prone class-wise knowledge coherence during prompt communication, which comprises two aspects.
First, as illustrated in Figure I (a), class knowledge across prompts from different clients inherently varies to some extent. During prompt communication (aggregation), this divergence often results in knowledge conflicts, degrading the model's acquisition capacity. Moreover, these conflicts exacerbate forgetting, as the aggregated prompts may conflict with the historical prompts. Second, as (b), the intra-class distribution disparity across clients often affects the learned semantics of prompts. Certain features, although locally discriminative, may prove suboptimal from global perspective, leading to further knowledge conflicts during prompt communication. Addi- tionally, these locally discriminative features may be confused with historical data representations, resulting in degraded performance on previously learned tasks.
depicted in Figure (b),
To address these challenges, we propose novel Class-aware Client Knowledge Interaction (C*Prompt) approach to improve inter-prompt class-wise relevance and intra-class distribution overlap simultaneously, as shown Figure (a)-bb) To achieve this, we first collect the local class distributions across clients and estimate the class-wise global distribution according to probability theory. Then, a local class distribution compensation mechanism (LCDC) is developed, which Iearns set of class prompts to transfer the local semantics the global domain, significantly improving intra-class knowledge consistency across clients. Additionally, each local prompt is recorded with its affinity with different classes. Then, class-aware prompt aggregation scheme (CPA) is designed to exploit the class affinities to estimate the class-wise knowledge relevance across prompts and generate dynamic weights to enhance class-relevant knowledge aggregation, effectively alleviating the confusion caused by class knowledge conflict. Extensive experiments on multiple FCL benchmarks demonstrate that CrPrompt outperforms state-of-the-art methods by large margins.
To summarize, the contributions of our paper are three-fold: (1) We present the CrPrompt, an exemplar- free method that achieves Class-aware Client Knowledge Interaction to mitigate the tem-

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poral and spatial forgetting simultaneously. (2) A local class distribution compensation mechanism developed to complement local distribution improve cross-client semantic consistency. (3) A class-aware prompt aggregation scheme is proposed to enhance intra-class knowledge aggregation and alleviate the knowledge conflict via a class-wise knowledge relevant estimation mechanism. (4) The superiority of Prompt is validated on the challenging FCL benchmarks, where our method consistently achieves remarkable state-of-the-art performance.
Related Work
In this section, we review three research directions and discuss the state-of-the-art works that are most relevant to this paper.
2.1 Federated Learning
FL considers a distributed machine learning paradigm where decentralized data resourees are modeled collaboratively (27 28 29 30). Each client trains with its corresponding data locally, and a server
aggregates the client knowledge to obtain global model B2 33 (3]. The key challenge in FL the data heterogeneity problem, where the data are not independently and identically distributed (non-III)) on different clients [35 [36
37, 38 Current FI. approaches can be primarily categorized into three branches, L.e., client-side regularization, server-side regularization, and synthetic data generation [39]. Client-side regularization methods aim to improve the alignment with the global model by refining local updates [40] 41 42
46 47). Server-side regularization approaches focus on achieving better aggregation to maximize the performance of the global model [8] 49) 500
43
44
45
5T]. Synthetic data generation methods rely on Mi or training a deep generation model to generate
synthetic data to approximate IID conditions [22 53 post-train the global model [54] [55 [56..
However, these FL methods assume that all the training data are available at the same time and neglect the practical condition that the training data occur sequentially.
2.2 Continual Learning
Continual Learning (CL) aims to learn with non-stationary data and generate a unified model
that can address multiple tasks ( 57 58]. Current CL methods are mainly divided into two categories: rehearsal- baesed and rehearsal-free Rehearsal-based methods [20:21 [2) save subset of learned samples into memory buffer and replay them when learning a new task. While promising performance has been achieved, they usually require a large memory cost and raise privacy concerns during long-term learning. Rehearsal-free methods dynamically expand the network or isolate parameters for different tasks, regularize the network parameters that are important to learned tasks. Recently, freezing the pre-trained backbone model and only training subset of learnable parameters is the current mainstream approach (59 60 8). L2P [62] pioneeringly introduced prompt learning to CL and proposed a key-query similarity method to select prompts for each task data from a prompt pool. CODAPrompt [1I] transforms prompt selection into differential process with attention mechanism. However, these approaches only consider alleviating temporal forgetting and struggle to address the non-IID data in the federated learning scenario 63
65
2.3 Federated Continual Learning
In FCL., each client continuously learns from a private and incremental task stream locally and a global
model aims to aggregate the spatial-temporal knowledge unified model B3] Existing FCL methods
primarily focus on generative replay to address the spatial and temporal forgetting [66] 18 6 67 (8). However, due to the slow convergence of generation training, training a generative model introduces massive training overheads [69]. Besides, the generative models typically risk privacy leakage of local information [6]. Recently, efficient tuning-bree based methods have shown advanced performance in FCL. PILoRA and LoRM introduced LoRA to address FCL by learning low-rank parameters each client and aggregates them in the server. Besides, prompt learning has shown remarkable mechanism that enables mutli-task knowledge co-consistency [70] 71 19]. However, existing methods typically neglect the knowledge conflict between individual prompts during server-side aggregation, leading significant knowledge loss.
anti-forge capacity due to the parameter-matcle
3

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Fromm
Stage R/Round
Stage t/Round 1-N,
Lourabbl
(a) Global Class Distribution Estimation
(b) Class-aware Prompt A ggreration
Server
Agyregoation
Disttibution
MAn.
Pex Mnn.
Cocmirtio Domgeting
IGN
Tanl bag cas
Pik Wolll bang Cat
EQ.10
CAS Gloda
15 Class Weggoed Prompt Agprepation
Cllert
Clieet
Ny Rounds
fz
/
Pix
Mon_ CRESee
Updae Beaadoy
Local Distribution
hig
Trstatiio Tiirsftrr
Lazalciom
Transfomeer
CLS tolees
Hasogram (H))...
Client
...
Client X
OCS)
Mucchn
QUx)
DEa
Reg
Image
Pi
Insernca Halteram 14
Imaage
(c) Local Class Distribution Compensation
Local Discriminativity Learning
Overview of our Prompt approach. During the training stage I, given the data Dr. each client k; the local class-aware feature distribution collected and uploaded to server to estimate the global distribution of each class. Then, the global distribution is distributed to the local clients to train client-specifit class-distribution compensation prompts Pi, During the later process, the discriminativity prompts Pf are introduced to learn classffication-releaatt knowledge, which are iteratively aggregated in the server according to the class knowledge relevance for N, rounds.
Figure 2:
Preliminaries
Problem Formulation: In FCL, collection of K clients collaboratively learn under the coordination of a central server. Each client &t E (1,2...., sequentially learns series of tasks. Let T denote the 1-th task of the k-th client, and DJ be its corresponding dataset. The model parameters of client during the learning of T are represented as of.
Following recent FCL methods [19] 722 19, we adopt CODAPrompt [1] as the basic architecture for both clients and the server. For each local task Th a set of prompts P Nx I,, is learned, where N is the number of local prompts, T.p is the length of each prompt, and D is the input dimension of the Vision Transformer (ViT) encoder. On the server side, a global prompt pool P. 5 N,, is maintained, containing prompts from both previous tasks Tire and the current the
Baseline:
task Teur, The total number of prompts in the pool denoted as N2, = x N, where number of seen tasks. For an image æ. its associated prompt P R. x D is generated through weighted sum of the prompts in Pg:
N = Σ a P
where the weights az faa,, a2, aM, } are computed based query-key similarity:
Ot (t(g(x) @ [Ag)i, [K,]), (g(x)))(((()))))))))))))))>> o [A, (
O [AgIN,: [KGl,,), (2)
where (-..) represents the cosine similarity function, K, E M3XD and A, E M2 are the learnable keys and attention weights of the prompts in P g. @ denotes the Hadamard product. For simplicity, given the one-to-one correspondence among Ag. Ky. and Pg. we represent the global prompt pool P encapsulating both attention and key representations.
In addition to the basic architecture of CODAPrompt, we also incorporate the knowledge distillation loss introduced by Powder [19 to enhance knowledge retention across tasks. The distillation loss is formulated as follows:
Jolgg Doul (er
Lad Der) = =
Σ
(3)
k= C.

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where Deu denotes the output logits of the current model, and Dtr represents the logits of the model at the beginning of the current communication round, containing the latest knowledge transferred from other tasks.
4 Proposed Method
In this section, we elaborate on our Prompt which primarily consist of four modules, ie., Global Class Distribution Estimation, Class-aware Prompt Aggregation, Local Class Distribution Compensa- tion, and Local Discriminativity Learning. An overview of Prrommpt is illustrated in Figure 2 and the procedure is summarized A Appendix
4.1 Global Distribution Generation
When the new stage data of client Dj is given, the local distribution for each class is first computed, resulting in distribution set D ( ) where (ui ki.)) denotes the class center and standard deviation, and C* is the number of classes for client &.. For each class the data distribution on client & is approximated by Gaussian distribution (e (af, Furthermore, the proportion of samples for class client & relative the global sample size of class represented as Pk.: The global mean and standard deviation are then aggregated across all clients as follows:
K (of)² = Σ (2 + (o pk. (2²)²
(5)
A=1
The theoretical derivations of Equation Jadd Equation 5 are provided in the Appendix A.1 Once calculated, the global distribution of each class sent back to each client for further processing.
4.2 Local Class Distribution Compensation
Upon receiving the global class distribution, local class distribution compensation prompts Pi
{pj)] )= are introduced to address the issue of local undersampling by transferring the local samples to align with the global distribution. Specifically, for each class i, local class distribution compensation prompt is denoted as O E x where Le represents the length of p,. Given an input image æ, it is first tokenized (73) into a sequence representation h E RL xd, where Ln the sequence length. The associated local class distribution compensation prompt of æ, denoted P is obtained by indexing from P. with its label. Both p and h are then fed into transformer layers:
fa,p - Soh Pe cls)),
(6)
where fo is the parameters of the pretrained ViT, cls the [CLS] token [59] and fe.p E R8 the generated feature. To ensure that fa.p aligns with the global class distribution, we assume that the global distribution for class follows a Gaussian parameterization N ((n (o))²). The alignment is enforced through distribution cross-entropy loss that maximizes the likelihood of Sa.p under the global distribution:
C. - -
(fz.p (EE)
u!?)
(7)
where Σ/ is diagonal covariance matrix with its diagonal entries equal 2. The theoretical derivations of Equation are provided in the Appendix A.2 Note that once Pi, {p.) is trained, it is frozen during the sequential rounds of training the current stage.
5

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4.3 Local Discriminativity Learning
When P,, is learned, we introduce the local discriminativity prompts Pi, (pi, 1 matrixed as r.A. which corresponds to prompts of original CODAPrompt, to enable new knowledge learning. Given and its label y. we generate instance-specific discriminativity prompt p form P according to Equation Besides, the local class distribution compensation prompt P is indexed from Pi,x using y. Then, cross entropy loss (CE) is introduced to optimize p
Lee &.f P Pu: cls)), v),
(8)
where We is the learnable weight of the classifier of client k. Note that P is exploited with p 0.5 to sufficiently utilize the information of both local original data and the completed distributions.
At the same time, instance histogram H | for æ is generated where {s²} denotes the similarity score between and p During round of training, introduce client histogram
i = {s])}=] j=1 for each prompt of stage that records the cumulative prompt-class matching scores *y. which is mathemtttcalyy calculated by:
iPed Σ
Ha.1y
where st represents the affinity between the prompt and class. Note that Hi can be generated online during training and requires almost no additional computing overhead. When one round of discriminative prompt training is finished, set of client histograms {1] 1N for the new stage prompts is uploaded to the server.
4.4 Class-aware Prompt Aggregation
When round of local discriminativity learning is finished, the local client histograms are collected to form a set Mi = { U (HA U...U (HH H. which is matrixed as H E IR Nx|c) Then, an inter-prompt correlation matrix Wt E RKKNKN computed by
W 9 H /T).
(10)
where 7 the softmax function that is conducted row-wise here, and a hyperparameter to scale the similarity scores. Besides, the prompts of stage are also collected from the clients to form set P W N. U Up U ... {pk N. i= which is matrixed P I N xd Then, a Class
Weighted Prompt Aggregation process is conducted by:
p = W'P' g'
(11)
where Pf+ TRKNNL, is the updated prompts that have collected the most relevant knowledge from prompts of different clients. Then, P split into prompt sets and distributed to the corresponding clients.
Training and Inference: As shown in Figure 2 during stage t, the training process consists of two phases. Firstly, Global Class Distribution Estimation and Local Class Distribution Compensation are conducted at round 0. The local distribution compensation loss La is adopted train Pi Then, from round to Nr, Class-aware Prompt Aggregation and Local Discriminativity Learning conducted in turn. The model optimized by an overall loss:
= + 3L
(12)
where 3 is hyperparameter balance the loss components.
During inference, following previous works [9), the prompts learned on the seen local tasks are collected to generate prompt Pæ which is exploited generate predictions by

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(13)
where W is the global classifier by concentrates the local classifiers learned from different tasks following 19
Experiments
5.1
Experimental Setups
We conduct the experiments three widely used benchmarks in FCL,, i.., ImageNet-R 744, DomainNet(75] and CIFAR-100[76] To evaluate the effectiveness of different FCL methods, 6 metrics are adopted in this paper, including Average Accuracy (Avg), Average Incremental Accuracy (AIA), Forgetting Measure (FM), Forward Transfer (FT), Backward Transfer (BT), Combined Transfer (CT). The configurations of the benchmarks and the details of the metrics are presented Appendix C
Datasets and Metrics:
Table
Result comparison on the ImageNet-R and DomainNet benchmark
ImageNet-R
DomainNet FT
Methods
Pub.
Avg
AIA
FM4
FT
BT1
CTf
Avgt
AIAf
FM,
BTT
CTf
FedWEIT
ICMI2201
71.10
74.30 1.80
-2.39
-1.83
-3.86
67.84 69.63
1.91
22.92
-3.11
-4.97
CFeD
UCAA2002
47.93
59.79
3.81
-17.67
-4.92-29.66
42.85 60.19
1.65
-4.98
-1.32
-15.64
GLFC
CVPR 2022
72.96
75.21
1.10
-3,87
-1.55
-5.11
69.75 70.34
1.23
-4.08
-2.46
-6.04
Fedspace
CVPR 2023
72.27
73.36
2.01
-2.60
-4.91
-5.95
68.98
70.71
1.80
1.87
-4.16
-1.45
Fed-L2P
CVPR2022
77.88
75.03
0.41
-2.79
-0.17
-2.92
70.98 72.36
0.16
-2.18
0.10
-2.09
Fed-Dual
ECCV2022
76.85
74.91 0.49
-3.12
0.22
-2.95
71.90 72.15 0.16 -1.82
0.41
-1.49
Fed-CODA
CVPR2023
79.65 75.14 14 -0.68
-2.53
1.69
-1.18
72.47 72.84 0.01 -0.82
0.83
-0.15
Fed-CP
JCML2023
76.75 72.59 0.63
-3.16
0.00
-3116
71.28 69.92 0.18 -2.78
0.00
-2.78
Powder
JCML2024
84.69
84.08 -0.54
4.48
1.95
6.04
75.98 77.28 0.10
1.28
0.14
1.40
PILoRA
ECCV2024
45.43
48.72 0.92
-5.75
-7.32
12.54
31.22 40.76 0.55
-0.12
-0.74
-1.81
Fed-MOS
AA12025
47.67
47.08 1.40
-3.30
-0.12
-3.37
40.37
45.22 0.31
-1.43
-1.21
-2.50
LoRM
ICLR2025
58.00
67.78
8.71
-4.67
-9.22
-13.70
23.18
28.49
5.72
-1.32
-0.11
-1.40
Prommt
This Paper
87.20 85.93 -0.36 7.63
1.12
8.52 78.88 77.55 -0.02 3.87 0.23
4.05
Compared Methods: We compare our proposed Proomt with the following methods: (1) Fully- Tuning-based (FULLY) federated continual learning methods, including FedWEIT [77), CFeD (78)
(EFFICIENT)) methods, including prompt learning approaches, FedCPrompt [71 and Powder [19]. Besides, the state-of-the-arr prompt-based continual learning methods, iee., 1.2P 62) DualPrompt [8] CODAPrompt [I]] are integrated with the well-known FedAvg [48] algorithm to make comprehensive comparison (Fed-L2P, L2P-Dual, Fed-CODAP, Pred-Prrompt.... Additionally, the LoRA-based FCL methods, including PILoRA [80] and LoRM [ and the adapter-based continual learning method MOS [82] is integrated with FedAvg to form Fed-MOS. All experiments are implemented using official code, with the ViT-B/16 pre-trained on ImageNet-21k serving the backbone network.
GLFC 7 and FedSpace 79.. (2) Efficient-Tuniin Saieeett-TTinngbaaaeea
Implementation Details The settings our diseriminativity prompts follow the configuration of previous works [19], where I,p. N and d are set to 10, 8 and 768, respectively. For our class distribution compensation prompt, the prompt length T., is set to 3 by default. The Adam optimizer with learning rate of 0.01 is adopted during training. For all the experiments, the training and testing images are resized to 224x224. The client number K and round number for each task are set 5 and 3, respectively. All experiments are conducted single Nvidia 4090 GPU.
5.2 Comparison Results
follow the experimental setting of the previous methods [19] and the comparison results on the ImageNet-R and DomainNet are represented in Table where Avg and AIA are the most important
7

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metrics indicating the long- term knowledge accumulation and progressive performance, respectively, The best and second best methods are highlighted in Bold and Underlined, separately.
Avg Comparison: Our Prompt outperforms the state-of-the-arl Powder, achieving improvements of 2.51 % and 2.90 % ImageNet-R and DomainNet, respectively. These results demonstrate the superiority of our method in long-term knowledge consolidation. This is because the new knowledge acquisition capacity is significantly improved with our local class distribution compensation and class-aware discriminativity prompt aggregation designs, Besides, the accurate knowledge communi- cation mechanism avoids the irrelevant prompts fusion that generate invalid prompts which not only semantically away from new prompts, but also conflict with historical prompts.
AIA Comparison: Our Prommt achieves improvements of 1.85% on ImageNet-R and also outperforms all existing approaches on DomainNet, verifying our method consistently ob- tains superior performance compared the existing methods across different training stages. This is attributed to the local class distribution compensation and class-aware discriminativity prompt aggregation designs that enhance robust local knowledge acquisition and improve distributed knowledge collection.
87.5
85.0
82.5
Ave.AC
80.0
77.5
75.0
72.5
2
Fed-CODAP, Powder and our PrPommpt show a negative for- getting rate on the small-scale dataset ImageNet-R. This indicates the new tasks can facilitate historical task learning when training samples are limited. On the large-scale dataset DomainNet, only our PrPommpt shows negative forgetting rate. These results verify the effective antiforgetting capacity of our method under different conditions.
FM Comparison:
stage
Fed-L2P
Fed-CODAP
GLFC
Powder
Fed-Dual
FedCPrompt
Fedspace
Figure 3: Avg-ACC curves on the seen tasks across training stages.
FT Comparison: Our Prommt shows advanced forward-transi capacity, outperforming existing methods by at least 3.15% % and 2.59 % on ImageNet-R and DomainNet, respectively. This is primarily attributed to the Global Class Distribution Estimation and Local Class Distribution Compensation designs, where the former can effectively exploit the asynchronously arriving data of the same class to generate reliable global distribution estimation. Then the estimated global distribution is exploited by the later to achieve data-level information compensation, thereby significantly improving subsequent data learning.
BT Comparison: Fed-CODAP, Pow- der and our C*Prompt consistently show positive backward-transfer across both ImageNet-R and DomainNet datasets. This is because the asynchronously arriving data enable the later tasks enhance the knowl- edge of previously seen classes. We ob- serve that the beekward-transiee results of Prroompt are relatively inferior to Fed- CODAP and Powder. This is because the Local Class Distribution Compensation and Class-aware Prompt Aggregation de- signs significantly improve the distributed data learning capacity at each stage, leaving less improve- ment space for seen tasks.
$4.69
Baseline
86.02
+CPA
$6.57
-LCDC
87.20
+CCDCCCPA
86
Avg-ACC (%)
Figure 4: Ablation on the model components.
As for the combined transfer of forward and backward, our C² PrPrompt outperforms all existing approaches with 2.48% and 2.65% improvements on ImageNet-R and DomainNet. re- spectively. These results demonstrate that the class-aware client knowledge interaction designs in this paper effectively boost the overall learning capacity in the temporal dimension in FCL. Specifically,
CT Comparison:

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Powder
Indiecintiatvve
Redurza Indieriminative
Altiation
Ours
Disnimenatice
Region
Figure 5: Visualization of attention across prompt and image regions.
the Global Class Distribution Estimation effectively aggregates the distributional information across spatial and temporal data sources. Local Class Distribution Compensation module leverages global distributional image to overcome the non-IID phenomenon across clients. Finally, Local Discriminativity Learning and Class-aware Prompt Aggregation modules effectively integrate the distributional knowledge into the prompts.
To further analyze the model learning process, visualize the Average Accuracy (Avg-ACC) across the seen tasks during the FCL stages in Fig. [) on the ImageNet- benchmark. The results show that our method consistently outperforms state-of-the-a approaches across all stages. Furthermore, the Avg-ACC of C Cr-Prompt exhibits stable upward trajectory throughout the training process, whereas other methods display either declining or fluctuating trends. This advantage is attributed to our class-aware client knowledge interaction designs, which effectively extract and preserve robust knowledge over long-term training. contrast, existing methods are more prone to knowledge conflicts during parameter aggregation in FCI., leading to performance degradation as training progresses.
Performance Tendency Analysis:
5.3 Ablation Study and Additional Analysis
Ablation on components. Since Fig. and Fig. (c) rely on each other, we present them unified component termed LCDC. Besides, Fig. (b) and Fig. (d) also rely on each other, we present them unified component termed CPA.
Aggregation
Bastise
CPAILEIC
The ablation studies on LCDC and CPA illustrated in Fig- 4 which are conducted with the ImageNet-R benchmark. When using LCDC module alone, our method obtains 1.88% improvement compared the baseline, verifying the effectiveness of the Global Class Distribution Estimation and Local Class Distribution Compensa- tion mechanism. Besides, CPA achieves
Round
Round 2
Round 3
Figure 6: Visualization of loss curves.
1.33% improvement compared to the baseline, demonstrating the effectiveness of our Class-aware Prompt Aggregation design. When all our modules are used together, model performance further improved with 2.51 % improvement. This is because LCDC and CPA achieve input-level class information compensation and feature extraction parameter-level knowledge communication. respectively. Therefore, they are complementary to each other.
We also visualize the loss of Baseline, CPA and CPA+LCDC in Figure o During round different methods primarily learn with new data and converge similarly. When the first aggregation is conducted. all the methods show improved loss due the parameter drift. CPA shows the least loss improvement due to the class-aware prompt fusion design that mitigates the knowledge conflict issue. CPA+LCDC shows large loss improvement because the class distribution Compensation design guides the model in the early stage. During the second aggregation, the loss improvement of CPA+LCDDC is significantly reduced since knowledge correlation between prompts improved after round After the training of round 3, both CPA and CPA+LCDC show significantly lower loss compared to the

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Baseline. Although CPA and CPA+LCDC obtain similar final losses, the performance of CPA+LCDC superior to CPA since LCDC improves the robustness of the learned knowledge.
Visualization of learned prompts: Figure 5 illustrates the prompt attention maps of our -romptt in comparison with state-of-the-ard Powder [9]. Specifically, the prompts generated by Powder
largely dominated by class-irrelevant knowledge and exhibit limited discriminative feature extraction capacity. In contrast, the prompts generated by our method effectively focus the discriminative regions and influence less on the class-irrelevant knowledge. These improvements are primarily attributed our Class-aware Prompt Aggregation mechanism, which effectively alleviates conflicted knowledge fusion during prompt aggregation.
Communication Overhead: Table
pares the communication and parameter overhead of C2-Prompt with state-of-the- art methods. Our approach demonstrates comparable communication and parame- ter costs with Powder, with only 0.6% and 6.8% increases, respectively. The ad- ditional communication overhead stems from the exchange of class distribution in- formation between the server and clients. However, due to the sparse distribution of classes, this overhead remains mini- mal. The slight increase in training pa- rameter count is attributed to the introduc- tion of local class distribution compensa- tion prompts, which are significantly fewer than the discriminative prompts commonly used existing methods. Note that Cr-Prompt does not introduce any additional parameters or computational overhead during inference, as only the discriminative prompts are employed for testing.
Table 2: Comparison of communication and additional parameter overhead.
Parameter
Methods
Communication
Training
Inference
Fed-L2P
686.69MB
3,96MB
3,96MB
Fed-Dual
621.78MB
4.73MB
4.73MB
Fed- Fed-CODAP
815.63MB
11,43MB
11.43MB
Crompt
815.63MB
11.43MB
11.43M
Powder
493.08MB
2.64MB
2.64MB
Prompt
496.01
2.82MB
2.64MB
Conclusion
In this paper, we propose Class-aware Client Knowledge Interaction Promp)),, which enhances the class-wise knowledge coherence between prompts across clients, significantly alleviating both temporal and spatial forgetting by mitigating the potential knowledge conffict during prompt commu- nication. CrPrompt introduces kinds of prompts, local class distribution compensation prompt and local discriminativity prompt. The former transfers local class features to global class-wiss distribution to improve the intra-class semantic consistency across clients. The latter learn discrimina- tion capacity with local data and aggregated with the ones from other clients in the server according the class-wise affinity, enabling class-wise knowledge enhancement while alleviating conflicts. Extensive experiments on the challenging FCL. benchmarks demonstrate that our method significantly outperforms the fate-of-thheard validating the effectiveness of our approach.
Our approach requires class distribution communication at the initial round. Although this operation incurs minimal overhead due the sparsity of distributional parameters, it introduces a minor communication cost. Furthermore, the prompt aggregation process generates client-specific prompts, slightly increasing computing and storage overhead compared to existing prompt- based methods. Nevertheless, this remains significantly more efficient than full fine-tuning approaches, the number of learnable parameters prompts substantially smaller than that of the entire feature extractor.
Limitation Discussion:
Acknowledgement
This work was supported by the National Key R&D Program of China 2024YFA14100000 the National Natural Science Foundation of China (6237601 1), and the China Postdoctoral Science Foundation (2025T180417).
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A Theoretical Justification of Distribution Operations
A.1 Server-side Distribution Aggregation
Federated continual learning (FCL.) involves decentralized clients collahoratively learning over sequential tasks. To enhance cross-client coherence, we estimate the global class distributions on the server by aggregating local class distributions from each client. Given local class distributions N(u, W N o2), ..... (p a with probability density functions fi (I), f2. (x). the global distribution is defined using the clients' sample frequencies P1, P2. Pns satisfying E 1.
The global class mean is calculated as:
-= 1=/linite + P2f2(z) + +
P PI4i + P2/22 + + Puffe
(14)
The global class variance is expressed as:
o² = = pp/2(x) p...))))...)). pp...)) x² dr 122
(15)
Then we have
0 -1 (of /1) + p2 (o2 +........ Pa (o 12 112
(16)
This aggregation provides comprehensive global distribution for each class, continuously updated as new tasks arrive. These global statistics are then communicated back clients to guide local prompt optimization, enhancing semantic consistency.
A.2 Local Class Distribution Compensation Loss
Upon receiving global class distributions, each client optimizes local class distribution compensation prompts. The derivation of the loss function for the prompt is as follows:
For class i, the global class distribution represented as Nugg. Egg. Specifically, for feature vector X E IRd of class i, its probability density is:
p(X M4. Σj) (2 E, -e (x-- 22 (x--s))
(17)
where 14 E &d the mean vector and E; E Raxd is a positive definite symmetric covariance matrix. When assuming independent feature dimensions, the covariance matrix reduces to diagonal form:
Σ, diag(o .,.,,, ...)) (id
(18)
with determinant and inverse matrix given by:
Σ
II j=1
of and
Σ diag
(19)
2 2
0
0
According to (7). the exponent expands to:
(X, pk)
(X -E, p4)
Σ
(20)
0²
Thus, the probability density function decomposes as:
2
1
(i lui (1)))000)
(21)
(2T) (II 033)
1/2
23

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In federated learning, client-generain features fz.p should align with the server's global class distribution N (uig Eny) The optimization objective becomes maximizing the logliikelihood:
Le = logp(ff. Σng)
(22)
Substituting (7 and expanding:
log Dogp(fpplii Egy) d (27) log E
1 2
2 [frx - 1g) ()))(E))((a) - 1b)) (23)
1
Minimizing the negative log- log-likelihood loss:
L. lo-ooo(ff..llppp Σny) d log(2T) + log اون + 2f20 [ "fe (b) (24)
1
Since the first two constant terms can be omitted for optimization, Le can be simplified to:
L. - n
-
-²y)
(25)
This objective encourages local features to match the global class distribution, effectively reducing inter-client distribution gaps and enhancing semantic coherence during federated updates.
A.3 Theoretical Implications
Overall, the proposed distribution operations theoretically guarantee smoother knowledge align- ment across clients by optimizing class-wise distribution coherence. This process stabilizes global knowledge representations throughout continual learning, improving the robustness of the learned knowledge and improving the aggregation compatibility between prompts from different clients, effectively mitigating spatial and temporal forgetting.
B Algorithm of the proposed approach
The overall process of our Prompt shown in Algorithm !
C Details of the datasets and evaluation metrics
C.1 Datasets
We use three image datasets commonly utilized in Federated Continual Learning (FCL) to evaluate our method: ImageNet-R, DomainNet, and CIFAR- 1000 ImageNet-R consists of 30,000 images from 200 categories, including challenging samples from ImageNet and newly collected samples with various styles. The dataset is divided into a training set with 24,000 images and test set with 6,000 images, and 20% the training set selected as validation set for tuning model parameters. DomainNet is a large dataset containing 600,000 images and 345 categories, spanning six different domains. CIFAR- 100 contains 50,000 training and 10,000 test-colored images for 100 classes, respectively.
C.2 Configuration of Federated Continual Learning Benchmarks
The benchmark configuration of this paper follows previous FCL method Powder [19]. Based on transferability (tasks have class overlap and each task contains only a small portion of each class's s data) and asynchrony, for ImageNet-R, each task randomly selects 20 classes (20% samples per class), distributed randomly across clients with varying round durations. For DomainNet, each task randomly selects 35 classes (2% samples per class due to closeness to pre-trained distribution, others same as ImageNet-R). We control task overlap by randomly selecting classes with the least overlap to
24

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Algorithm Local Class Distribution Compensation and Global Class Distribution Estimation
Input: Stage / data D {D)
Output: Global prompt pool P
Initialize Pi = None for each round to N, do if round r 0 then
# Local Class Distribution Compensation (LCDC)
for each client do Compute local class statistics (ui k,,) for each class i Upload ( to the server end for
# Global Class Distribution Estimation Estimate global class center 1u = >K MiPPPh Eq. 4
Estimate global class variance (a = Σk=n (uf (ot ph. (KE)), Eq
Distribute the global distribution to the corresponding clients
# Back to (LCDC) for each client
K do
Initialize local class distribution complension prompts Pi [pp] ICH
For input æ, obtain fa. - fo((n P [CLS]]), Eq
Update Pi, using Le UU2 1 ( end for Froze prompt Pi,k end if
(
p4)) Eq.
# Local Discriminativity Learning for each client & to do if Pddt + None then
Initialize local discriminativity prompts Pi, (pp) with Pide end if
For each input x, phtain p Ph and H Update using Lee = (W(W&ffe (h P P cls)). y). Eq. 8
|c| Obtain client histogram H = {sc f for prompt p/z. where og = >iPa, [H Eq.
Upload (H and Pdd to the server end for
# Class-aware Prompt Aggregation (CPA) Server collects all client histograms to form H. E KN
Server collects all client discriminativity prompts to form P
Compute inter-prompt attention: W HHHH /T), Eq.
Update prompts: p WiP,, g> Eq. 11
Distribute Pd t,ke to corresponding clients
end for Return P
study FCL performance under different task correlations. Unlike the common Dirichler distribution method FI., we avoid it here because in FCL,, class sets of different tasks vary greatly, making hard to control similarity with it. The setup details for the CIFAR-100 dataset are the same as
25

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those for ImageNet-R. Simultaneously, we set five clients for training, each executing distinct tasks. Furthermore, we organize the training process into phases, each consisting of three communication rounds. At the beginning of each phase, 40% of the clients are selected to initiate learning new tasks. To ensure the fairness of the results, we keep the optimizer, learning rate, and local training epochs consistent with those of the Powder method when training the classification prompts. Our LCDC is trained using the Adam optimizer when new tasks arrive, and the trained prompt is only used for the training of the formal classification prompt and not for the testing phase.
C.3 Evaluation Metries
We evaluate the effectiveness our method by adapting seven metrics, including the Average accuracy of all tasks (Avg), Average Incremental Accuracy (AIA) [3], Forgetting Measure (FM) (84)
Forward Transfer (85), Backward Transfer (8T) Combined Transfer (CT), Final Average Accuracy (FAA).
Average accuracy of all tasks (Avg) across all tasks, computed as
This metric measures the average accuracy of the final model
Σ of (.max(R)
Avg T
TEET
where T denotes the set of all tasks during the Federated Continual Learning (FCL) process, and a' (,,)ax(R) denotes the final accuracy task Ted (i.c., the accuracy on this task when training con- cludes).
Average Incremental Accuracy (ATA) process, computed
This metric measures the average accuracy over the FCL
AIA I
Σ
der
R
PER T.ET.
where denotes the set of rounds with task switch, T, denotes the set of existing tasks at round r, and orr denotes the accuracy of T round r.
Forgetting Measure (FM) Forgetting is measured by the difference between the highest historical accuracy and the current accuracy of task. This metric quantifies the model's memory stability by the average forgetting over the FCL process, computed as
FM
Σ
der der
R
PER
T.ET.
where âz,r denotes the max accuracy of T before round
Forward Transfer (FT) This metric assesses the model's 's ability to transfer knowledge into task, from both previously learned tasks and other currently learned tasks, computed as
FT
Σ
(a a.)
TEET
where T denotes all tasks during the FCL process, å denotes the accuracy of T when it finished, and â denotes the accuracy of single-task training.
Backward Transfer (BT) This metric evaluates the model's ability to transfer knowledge from new tasks back to previously learned tasks, computed
BT
Σ
(CC_mux(()) - db)
T
TEET
where a d)))ax(R) denotes the final accuracy of T..
26

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Combined Transfer (CT) This metric is combination of FT and BT, evaluating the amount of information that task Te acquires from other tasks. The other tasks can have any sequence relationship with task T in terms of temporal dimension. It is computed as
CT ITI
Σ TEET
(a (ommax(R)) - -a²)
Final Average Accuracy (FAA) FAA is standard metric used in FCIL to measure knowledge retention and accumulation. Let a' denote the test accuracy on the t-th task after the final incremental step. FAA defined
FAA
where T is the total number of tasks. A higher FAA indicates better overall performance across all tasks and stronger continual learning ability.
D Results under Other Federated Incremental Learning Experimental Settings
In addition to the FCL setting proposed by Powder (ICML [9) which considers task overlaps over time, we evaluate our approach under the federated class incremental learning (FCIL) setting, as investigated by PILoRA (ECCV 2024) [0] and LoRM (ICLR 2025) [81.
FCIL Setting: The FCIL setting divides the learning process into 10 incremental tasks, where class distributions are disjoint across tasks. For each task, training data is distributed among 10 clients following Dirichlet distribution with parameter {0.5, 0.1, 0.05) simulate non-llD scenarios. A smaller 3 value represents a stronger data imbalance among clients.
Training Details: A VIT-B/16 backbone pre-traines trained on ImageNet-21 is adopted. Each communica- tion round consists of training epochs, with a total of communication rounds. Data augmentation for the training set includes random horizontal flipping and normalization. For the test set, prepro- cessing involves resizing with bicubic interpolation to 256 x 256, followed by center cropping to 224 x 224 and normalization.
Comparison Results: We compare our Prommp with state-of- the-art FCIL methods [86 87 62
11 88; 89 90 91 68, 80; 81] and the FCL method Powder [19] the ImageNet-R benchmark. The Final Average Accuracy (FAA) [8]] results are presented in Table 3 The results demonstrate that our Prommm surpasses the state-of-the-ard FCIL method LoRM, achieving improvements of 2.75 /5.91 % /2.29 % at B = 0.5/0.1 1/////11///// 10.055, respectively. Furthermore, compared to the state-of-the- art prompt-based based FCL method Powder, approach achieves 0.61 %/2.60% % improvements at = 1..5/0.1/.... respectively. These increasing advantages under lower / values are attributed the effective inter-client intra-class distribution knowledge compensation mechanism, which significantly enhances model acquisition capacity and mitigates inter-client knowledge conflicts. These findings, alongside the experiments reported in the main paper, validate the adaptability of our approach to diverse practical federated continual lcarning scenarios.
E Experimental comparison on Cifar100
Avg Comparison: Only our Prommt outperforms the state-of-the-art Powder, achieving improve- ments of 1.54% on Cifar100. This finding highlights the advantage of our approach in long-term knowledge consolidation. This can be attributed to the substantial enhancement of new knowledge acquisition capability achieved through our local class distribution compensation and class-aware discriminativity prompt aggregation strategies. Additionally, the precise knowledge communication mechanism prevents the fusion of irrelevant prompts, which would otherwise produce invalid prompts that are not only semantically divergent from new prompts but also clash with historical prompts.
Our Prrompp achieves improvements of 0.86% on Cifar100, confirming that our approach consistently outperforms existing methods across various training stages. These improvements due to the local class distribution compensation and class-aware discriminativity
AIA Comparison:
27

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Table 3: Performance comparison on ImageNet-R with different values.
ImageNet-R (FAA)
Method
Publication
0.5
0.1
8 0.05
EWC 86
NAS 2017
58.93
48.15
43.68
LwF 87
PAMI 2017
54.03
41.02
46.07
FisherAVG 88
NewrIPS 2022
58.68
50.82
47.33
RegMcan 89
ICLR
61.18
57.00
55,80
CCVR 900
NewrIPS 2021
70.00
62.60
60.38
L2P 662
CVPR 2022
42.08
23.85
16.98
CODA-P
CVPR 2023
61.18
36.73
25.82
FedProto
9
2022
58.52
47.30
52.93
TARGET 668
ICCV 2023
54.65
45.83
41,32
PILoRA
80
ECCV 2024
53.67
51.62
49.37
Powder
19
ICML 2024
74.62
67.14
62.26
LoRM
81
ICLR 2025
72.48
63.83
66.45
PrPommpt
This Paper
75.23
69.74
68.74
Table 4: Performance comparison on CIFAR-100 with different / values.
CIFAR-100 (FAA)
Method
Publication
0.5
0.1
8 0.05
EWC 86
2017
78.46
72.42
64.51
LwF 87
PAMI 2017
62.87
55.56
47.09
FisherAVG
88
NeuriPS 2022
76.10
74.43
65.31
RegMean 89
ICLR 2023
59.80
45.88
39.08
CCVR 90
NewrIPS 2021
79.95
75.14
65.30
L2P 662
CVPR 2022
83.88
61.54
55.00
CODA-P
CVPR 2023
82.25
61.82
46.74
FedProto 99
75.79
70.02
60.55
TARGET (68
ICCV 2023
74.72
72.32
62.60
PILoRA
80
ECCV 2024
76.48
75.81
74.80
Powder 19
ICMI, 2024
87.46
85.33
82.03
LoRM
81
ICLR 2025
86.95
81.76
82.76
CrPrrmpt
This Paper
89.93
87.67
83.25
prompt aggregation designs, which strengthen robust local knowledge acquisition and enhance distributed knowledge collection.
FM Comparison: Our Prompt shows negative forgetting rate the small-scale dataset Cifar 100. This suggests that new tasks can facilitate the learning of historical tasks when training samples are limited. These results confirm the effective antiforgetting capability of our method.
FT Comparison: Our Crommt shows advanced forward-transfed capacity, outperforming existing methods on Cifar100, respectively. This can be primarily attributed to two key components: the Global Class Distribution Estimation and Local Class Distribution Compensation mechanisms. Specifically, the former effectively leverages asynchronously arriving data from the same class to generate reliable global distribution estimates, while the latter utilizes these estimated global distributions to implement data-level information compensation, thereby significantly enhancing the learning efficiency subsequent data.
consistently demonstrates positive backward transfer capability on Cifar1 00 dataset. This arises from the fact that asynchronously arriving data allow subsequent tasks to enhance knowledge of previously seen classes. We observe that C*Prompt's backward- transfer results relatively outperform those of Fed-CODAP and Powder. This is because the Local Class Distribution Compensation and Class-award Prompt Aggregation designs significantly boost
BT Comparison: Our Proomtpt
28

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Table 5:
Result comparison the CIFAR-100 benchmark
Methods
Publication
Avgt
AIA
FM4
FT
BT
CT1
FedWEIT
ICML
95.17
95.61
0.48
3.76
-0.91
3.04
CFeD
IICA 2022
73.87
79.06
2.07
-11.01
-4.71
14.78
GLFC
CVPR 2022
95.35
95.92
0.35
5.51
-0.54
5.08
Fedspace
CVPR 2023
94.17
94.87
1.03
0.37
-2.46
-1.60
Fed-L2P
CVPR
95.65
95.68
0.08
0.89
0.08
0.95
Fed-Dual
ECCV 2022
95.35
95.08
0.27
0.70
-0.24
0.51
Fed-CODAP
CVPR 2023
82.05
55.71
13.77
-30.25
-8.60
45.13
FedCPrompt
JCML
94.22
94.04
0.08
0.32
0.00
0.32
Powder
JCML 2024
95.78
95.83
0.35
2.03
-0.36
1.74
PILoRA
ECCV 2024
76.21
82.31
0.32
0.01
-0.42
-0.40
Fed-MOS
AAAT 2025
85.11
87.23
0.20
-0.31
-0.11
-0.45
LoRM
77.42
80.11
0.74
0.07
0.20
0.22
Ours
This Paper
97.32
96.78
-0.05
2.57
0.31
2.82
87.25
87.25
87.00
87.00
ACC (%)
86.75
ACC (%)
86.75
86.50
86.25
86.25
86.00
86.00
0.4
Lc
p
Figure 7: Ablation studies on the hyper-parameterss under ImageNet-R dataset.
the distributed data learning capability at each stage, thereby leaving less improvement space for seen tasks.
CT Comparison: In terms of the comprehensive performance of forward and backward transfer, Prrommpt overall outperforms other existing methods that employ efficient fine-tuning. These results demonstrate that the class-aware client knowledge interaction designs proposed in this paper effectively enhance the overall learning capability of Federated Continual Learning (FCL) in the temporal dimension. Specifically: the Global Class Distribution Estimation module efficiently aggre- gates distributional information across spatial and temporal data sources; the Local Class Distribution Compensation module utilizes global distribution representations to overcome the non-IID (non- independent and identically distributed) phenomenon across clients; and the Local Discriminativity Learning and Class-aware Prompt Aggregation modules effectively integrate distributional knowledge into prompts.
Analysis on the hyper-parameters
In Figure we evaluate the performance of Prommt under different values of the hyper- hyper-paramene parameters Le and p. The parameter L. represents the length of the local class distribution compensation prompts. When the prompt length is less than or equal to 3, larger value of parameter enables the trained prompts to better the central distribution the class, thereby improving model's performance. Meanwhile, serves as the usage probability of local class distribution compensation prompts, is used to determine the number of generated new central distribution samples. Based on experimental analysis, the optimal hyperparameter value for p set to 0.3.
29

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Powder
Ours
Figure 8: Prompt attention visualization ImageNet-R.
Powder
Outs
Figure 9: Prompt attention visualization on DomainNet.
G
Prompt attention visualization comparison on different benchmarks
Figure and 9 the visualization comparison of prompt attention maps between our Proommpt framework and the state-of-the-art Powder method across the challenging ImageNet-R and DomainNet benchmarks. The results demonstrate that prompts generated by Powder are predomi- nantly influenced by class-irrelevant knowledge, leading to limited discriminative feature extraction capabilities. In contrast, our method effectively focuses prompt activations discriminative regions while significantly reducing interference from aaassaagosticc knowledge. These improvements are pri- marily attributed to the proposed Class-aware Prompt Aggregation mechanism, which systematically alleviates the fusion of knowledge conflicts during prompt aggregation through explicit semantic alignment.
H Broader Impacts
Our method tackles practical federated continual learning (FCL) problem and introduces a novel approach that effectively improves the local parameter learning in the client side and enhances knowledge aggregation capacity on the server side.
The Potential Positive Societal Impacts of this research include:
Enhanced Privacy Preservation Decentralized Learning
Federated continual learning (FCL) inherently avoids centralized data collection. Crrormpt further eliminates reliance on raw data generative models for knowledge retention, reducing risks of sensitive data leakage. This critical for applications like healthcare (e.g., personalized disease prediction across hospitals) finance (e.g., fraud detection without sharing transaction details).
Improved Adaptability in Dynamic Environments
By addressing both temporal and spatial forgetting, CrPoompt enables models to continuously adapt evolving data streams. This ensures long-term reliability in scenarios where data distributions shift over time or vary across regions.
3. Democratization Al Resource-Constriie Settings
The lightweight prompt-based framework reduces computational and communication overhead compared to traditional methods. This democratizes access to AI for edge devices with limited
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resources (e.g., rural IoT sensors, low-power medical devices), fostering equitable technological progress.
Mitigation of Model Bias via Class-Aware Aggregation
The class-aware prompt aggregation (CPA) mechanism explicitly accounts for inter-client class relevance, potentially reducing biases arising from skewed local data distributions. For instance, in facial recognition systems deployed across diverse demographics, CPA could improve fairness by ensuring minority groups' features are adequately represented.
The Potential Negative Societal Impacts of this research include:
1. Energy Consumption
Additional distributional information communication and client-wise aggregation across distributed clients may increase energy consumption, particularly in large-scale deployments.
31


Reviews:
[{"review_id": "F1g3v2XMDb", "reviewer": "Reviewer_nsjy", "summary": "This paper proposes C$^2$Prompt, a novel class-aware client knowledge interaction method for FCL. It addresses the key challenges of temporal forgetting and spatial forgetting by enhancing class-wise knowledge coherence across distributed clients. The approach involves two main components: a local class distribution compensation mechanism (LCDC), which reduces intra-class distribution disparities among clients to improve intra-class semantic consistency; and a class-aware prompt aggregation scheme (CPA), which estimates class-wise relevance to better align prompts globally and mitigate knowledge conflicts. Extensive experiments on multiple FCL benchmarks demonstrate that C$^2$Prompt significantly outperforms existing methods.", "questions": "1.I want to know whether the uploaded local class-aware feature distributions will cause privacy leakage.\n\n2.Why are the locations of indiscriminative attention of the proposed method similar to those of the Powder in Figure 5? For example, the two points above the first image.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1.The paper thoroughly discusses the issues of knowledge coherence in federated prompts and provides well-motivated mechanisms to address these, supported by theoretical considerations.\n\n2.The paper considers an important scenario: continuous learning in the federated scenario.\n\nWeaknesses:\n\n1.The layout needs to be corrected. For example, Figure 5 appears before Figure 3. Figure 6 is mentioned before Figure 5 in Line 319.\n\n2.There are many clerical errors. For example, Fig. 2 (a) and Fig. 2 (c) in Lines 303 and 305.\n\n3.Lack of experiments on different models. This paper only considered one model, i.e., ViT-B/16 backbone pre-trained on ImageNet-21K. It is better to consider the impact of larger or smaller model architectures and out-of-distribution pre-training datasets.\n\n4.Equation 13 remains rather unclear. What is the relationship between the prediction of an image and $L_{ce}$ during the inference stage?\n\n5.In the experiments, the training tasks are sampled from each dataset.  It is better to investigate the scenario where different tasks are sampled across different datasets. For example, task 1 is a 20-classification task sampled from ImageNet-R, and task 2 is a 10-classification task sampled from CIFAR-100.", "was_revised": true, "final_justification": "Thanks for the clarification and additional evaluation. The authors' rebuttal has addressed my concerns, so I will increase my score to 4.  I recommend incorporating these experiments into the final version, as this would make the paper more comprehensive and persuasive.", "rebuttal": "Thanks for the valuable feedback. We hope our responses effectively address your concerns. \n\n**W1: Layout correction**\n\nThank you for the suggestion. We have revised the manuscript layout to ensure that all figures appear in the correct order and align properly with the corresponding textual references.\n\n**W2:  Clerical errors**\n\nThank you for pointing this out. The references to Fig. 2(a) and Fig. 2(c) in Lines 305–306 should have been Fig. 2(b) and Fig. 2(d), respectively. We have corrected this typo in the revised version.\n\n**W3-1: Larger or smaller model architectures**\n\n(1) Larger Architectures:\n\nWe have adopted ViT-L as the backbone and compared our method with the state-of-the-art Powder. The results are as follows:\n\n|DomainNet/ViT-L|Avg|AIA|\n|-|-|-|\n|Powder|77.75|77.11|\n|Ours|79.55|77.72\n\n|ImageNet-R/ViT-L|Avg|AIA|\n|-|-|-|\n|Powder|87.52|85.79\n|Ours|87.94|85.80\n\nOur method achieves **1.80%**/**0.61%** improvements on DomainNet and **0.42%**/**0.01%** improvement on ImageNet-R under the Avg/AIA metrics compared to Powder. \n\nThe more significant improvement on DomainNet can be attributed to its larger domain gap from the pretraining dataset (ImageNet-21K). In this case, our class-aware client knowledge interaction mechanism improves knowledge acquisition by **enhancing new domain distribution adaptation**. \n\nIn contrast, the relatively modest gain on ImageNet-R is due to the strong alignment between ImageNet-R and the pretraining dataset. ViT-L can already capture a large portion of relevant knowledge during pretraining, leaving limited room for post-training improvement. Nevertheless, the consistent performance gains on both benchmarks verify the effectiveness of our method when using large-scale architectures.\n\n(2) Smaller Architectures:\n\nWe have also evaluated our method using ViT-S as the backbone. The results are shown below:\n\n|DomainNet/ViT-S|Avg|AIA|\n|-|-|-|\n|Powder|75.16|73.80\n|Ours|76.44|75.47\n\n|ImageNet-R/ViT-S|Avg|AIA|\n|-|-|-|\n|Powder|81.92|81.47|\n|Ours|83.43|82.69\n\nOur method outperforms Powder by **1.28**/**1.67** on DomainNet and **1.51**/**1.22** on ImageNet-R under Avg/AIA metrics, demonstrating its adaptability to small-scale architectures.\n\n(3) Summary:\n\nThese results confirm that our approach is **robust across different model scales** and consistently outperforms the state-of-the-art Powder. This is mainly because our class-aware client knowledge interaction mechanism leverages distributional information, which is largely independent of the backbone scale.\n\n**W3-2: Out-of-distribution pre-training datasets**\n\n(1) We would like to clarify that all reported benchmarks, DomainNet, ImageNet-R, and CIFAR-100, **are out-of-distribution datasets relative to the pre-training dataset ImageNet-21K** [a]. This is because part (ImageNet-R) or all (DomainNet, CIFAR-100) of their images do not appear in ImageNet-21K, and they typically exhibit significant domain shifts from the pre-training distribution.\n\n[a] What Variables Affect Out-of-Distribution Generalization in Pretrained Models? NeurIPS 2024\n\n(2) To further address the reviewer's concern, we additionally evaluate our method in another out-of-distribution setting by adopting **CLIP** as the backbone and conducting post-training on **unseen benchmarks** [b].\n\n[b] SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting. CVPR 2025\n\nThe results are presented below:\n|DomainNet/CLIP|Avg|AIA|\n|-|-|-|\n|Powder|82.29|82.43|\n|Ours|84.18|83.71\n\n|ImageNet-R/CLIP|Avg|AIA|\n|-|-|-|\n|Powder|89.44|89.53\n|Ours|90.48|90.01\n\nOur method achieves **1.89%**/**1.28%** Avg/AIA improvements on DomainNet and **1.04%**/**0.48%** on ImageNet-R, respectively. These results demonstrate that our method is not dependent on the pre-training distribution and generalizes well under out-of-distribution scenarios.\n\nThis robustness is primarily attributed to the fact that many high-level visual semantics (such as shape, structure, and layout) are transferable across domains. Our Local Distribution Compensation and Class-Weighted Prompt Aggregation modules are capable of ** exploiting such domain-invariant cues**.  By modeling the global distribution and adjusting local distributions on clients, they enable effective adaptation from pre-training models to unseen target domains in the federated continual learning scenarios.\n\n**W4: Equation 13**\n\nThank you for pointing this out. The notation $\\mathcal{L}_{ce}$ in Equation 13 is indeed a typo. It should be corrected to $\\hat{y}$, which denotes the predicted class probabilities generated by the model during inference. We have carefully corrected this issue in the revised version.\n\n**W5: Cross-dataset sampling**\n\n(1) We have conducted additional experiments by alternately sampling task classes from ImageNet-R and CIFAR-100, as suggested. The results are summarized below:\n\n|ImageNet-R+CIFAR-100|Avg|AIA|\n|-|-|-|\n|Powder|90.80|92.68|\n|Ours|93.18|93.31|\n\nOur method achieves **93.18%** and **93.31%** on the Avg and AIA metrics, respectively, outperforming the state-of-the-art Powder by **2.38%** and **0.63%**. These results demonstrate that our approach generalizes effectively under the more challenging cross-dataset federated incremental learning scenario. This robustness partially stems from the prompt learning scheme, which helps disentangle knowledge across datasets and mitigates mutual interference. In addition, the performance gain over Powder is primarily attributed to our Local Class Distribution Compensation and Class-Weighted Prompt Aggregation designs, which enhance the model's ability to learn and accumulate knowledge from clients at each stage.\n\n**Q1: Privacy leakage**\n\nThe uploaded local class-aware feature distributions **do not** cause privacy leakage\n\n(1) The uploaded distributions only contain the mean and variance of each local class. **No instance-level information or raw data** is transmitted to the server, ensuring that no individual-specific information is exposed.\n\n(2) These uploaded statistics are equivalent to distributional prototypes, a widely adopted strategy in **privacy-preserving learning**. Similar approaches have been employed in existing privacy-friendly methods such as [c,d].\n\n[c] Fecam: Exploiting the heterogeneity of class distributions in nonexemplar continual learning. NeurIPS 2024\n\n[d] Distribution-aware knowledge prototyping for non-exemplar lifelong person re-identification. CVPR 2024\n\n**Q2: Indiscriminative attention**\n\n(1) The presence of indiscriminative attention patterns similar to those in Powder is expected, as Powder serves as our baseline. Our method inherits the local discriminative prompt from Powder, which may include suboptimal attention behaviors. However, our approach mitigates the impact of indiscriminative knowledge by enhancing the acquisition and accumulation of discriminative information as discussed below.\n\n(2) We further observe that the locations of indiscriminative attention tend to be shared across different images, e.g., the two hotspots above the first, second, and sixth images. This phenomenon stems from dataset bias, where certain non-informative regions frequently appear in the same spatial positions across images, thus leading to spuriously high attention scores.\n\n(3) Our method significantly **reduces such indiscriminative attention** through the following designs:\n\n(i) The Local Class Distribution Compensation module actively models the global distribution of each class. This **enhances both intra-client discriminative knowledge learning and inter-client knowledge compatibility**, thereby facilitating more discriminative knowledge aggregation.\n \n(ii) The Class-aware Prompt Aggregation mechanism selectively retains class-specific prompts while avoiding inter-class knowledge conflicts during aggregation. This strengthens discriminative feature extraction and **suppresses noisy or irrelevant information**.\n\nIn summary, our proposed method primarily overcomes the influence of indiscriminative attention inherited from the baseline by significantly improving the model's capacity to **acquire and accumulate discriminative knowledge**.\n\nWe sincerely appreciate your thoughtful feedback and hope the above clarifications are helpful. We welcome further discussion and are grateful for the opportunity to improve our work through your insights. Thank you again for your time and consideration."}, {"review_id": "qyQ4IpGcpY", "reviewer": "Reviewer_gSYz", "summary": "This paper addresses the challenges of spatio-temporal forgetting in Federated Continual Learning (FCL) by enhancing class-wise knowledge coherence across clients. The authors propose C2Prompt, a prompt-based framework introducing two novel modules: (1) Local Class Distribution Compensation to mitigate intra-class distribution disparity, and (2) Class-aware Prompt Aggregation to reduce inter-class knowledge conflict. Experiments demonstrate the effectiveness of the proposed method.", "questions": "The authors are expected to address the concerns in the \"weaknesses\".", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper identifies two underexplored but crucial sources of knowledge degradation in prompt-based FCL: intra-class distribution gaps and inter-prompt class relevance.\n2. The proposed method outperforms several state-of-the-art approaches across six evaluation metrics.\n3. The paper is well written and easy to follow.\n4. Theoretical justification is provided to further demonstrate the usefulness of the designs in the proposed paper.\n\nWeaknesses:\n1. More datasets are expected for a comprehensive comparison.\n2. The efficiency and communication costs of the proposed method (compared to baselines) can be further discussed.\n3. The experiments on scenarios with severe data heterogeneity are expected.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback and insightful comments. We address your concerns as follows:\n\n**W1: More datasets for comparison**\n\n(1) In the main paper, we report experimental results on DomainNet and ImageNet-R in Table 1, following the settings in Powder [a].\n\n(2) In **Table 4** of our appendix, we evaluate our method on the **CIFAR-100** dataset. Our approach outperforms the state-of-the-art method Powder by **1.54%** on the Avg metric and **0.86%** on the AIA metric. \n\n(3) We further conduct experiments on the ImageNet-A dataset. The results are summarized below:\n|ImageNet-A|Avg|AIA|\n|-|-|-|\n|Powder|66.35|63.14\n|Ours|68.34|65.34\n\nOur method surpasses Powder by **1.99%** on Avg and **2.20%** on AIA, further confirming its robustness on challenging real-world benchmarks.\n\n(4) These consistent improvements across multiple benchmarks verify the adaptability and generalizability of our approach. The performance gain primarily stems from our two core designs:\n\n(i) Local Class Distribution Compensation (LCDC) actively models the global distribution of each class, thereby **improving local discriminative knowledge acquisition and enhancing inter-client knowledge compatibility**. This results in a more robust and informative prompt aggregation process.\n\n(ii) Class-aware Prompt Aggregation (CPA) selectively filters and preserves class-specific prompts, effectively **mitigating inter-class knowledge interference during aggregation**. This design effectively consolidates the discriminative capacity of the learned features across clients.\n\nTogether, these modules enable effective distribution modeling at both global and local levels, enabling our model to boost performance on diverse benchmarks.\n\n[a] Federated Continual Learning via Prompt-based Dual Knowledge Transfer. ICML 2024\n\n**W2: Efficiency and communication costs**\n\nWe report communication and parameter overhead in **Table 2** of the main paper. The results show our method maintains strong efficiency:\n\n(1) Communication Overhead:\n\nOur method requires 496.01MB for communication. Compared with prior prompt-based methods (FED-L2P, FED-DUAL, Fed-CODAP, Fed-CPrompt), this is **125.77MB to 319.62MB** lower, yielding a **25.4% to 64.4% reduction**. Compared to Powder, our method incurs only **2.93MB (0.6%)** additional overhead, mainly due to the exchange of lightweight class distribution information. Since the number of classes is small, this additional cost is negligible.\n\n(2) Training Overhead:\n\nOur method introduces 2.82MB of learnable parameters. Compared with other baselines, this is **1.14MB to 8.61MB fewer**, leading to **40.4% to 305.3%** better training efficiency. Compared with Powder, our method adds only **0.18MB (6.8%)**, resulting from the lightweight distribution prompts introduced by the LCDC module.\n\n(3) Inference Overhead:\n\nAt inference time, our method requires only **2.64MB** of additional parameters, which is the same as Powder and **50% to 333.0%** more efficient than other prompt learning baselines. This is because only discriminative prompts are retained during inference, incurring **no additional cost compared to Powder**.\n\n**W3: Experiments with severe data heterogeneity**\n\n(1) In Table 3 of our appendix, we report experimental results on the ImageNet-R dataset under varying levels of data heterogeneity using the Dirichlet distribution parameter $\\beta$ ranging from 0.5 to 0.05. Notably, $\\beta=0.05$ is typically regarded as a setting with severe data heterogeneity [b]. Under this condition, our method achieves a **2.29%** performance gain over the state-of-the-art LoRM [b], and a substantial **6.48%** improvement compared to the prompt-based baseline Powder.\n\n[b] Closed-form merging of parameter-efficient modules for federated continual learning. ICLR 2025\n\n(2) We further evaluate our method on the CIFAR-100 and ImageNet-A datasets under various levels of data heterogeneity. The results are summarized below:\n\n|CIFAR-100|$\\beta$=0.5|$\\beta$=0.1|$\\beta$=0.05|\n|-|-|-|-|\n|LoRM|86.95|81.76|82.03\n|Powder|87.46|85.33|82.76\n|Ours|89.93|87.67|83.25\n\nThe results demonstrate that our method surpasses the state-of-the-art FCIL method LoRM, achieving improvements of **2.98%/5.91%/1.22%** at $\\beta = 0.5/0.1/0.05$, respectively. Furthermore, compared to the state-of-the-art prompt-based FCL method Powder, our approach achieves **2.47%/2.34%/0.49%** improvements at $\\beta = 0.5/0.1/0.05$, respectively. \n\n\n|ImageNet-A|$\\beta$=0.5|$\\beta$=0.1|$\\beta$=0.05|\n|-|-|-|-|\n|LoRM|37.26|36.34|33.11\n|Powder|39.29|37.11|35.62\n|Ours|40.51|39.76|37.21\n\nOn ImageNet-A, our method surpasses LoRM by **3.25%/3.42%/4.10%** and Powder by **1.22%/2.65%/1.59%** at $\\beta = 0.5/0.1/0.05$, respectively. \n\n(3) These results clearly demonstrate that our method maintains robust performance across varying degrees of data heterogeneity, particularly under severe heterogeneity conditions (i.e., small $\\beta$ values). The advantages over the Powder baseline in lower $\\beta$ regimes can be attributed to our inter-client, intra-class distribution compensation mechanism, which effectively aligns client-specific distributions, enhances the model's knowledge acquisition capability, and reduces inter-client knowledge conflicts.\n\n\nWe sincerely appreciate the reviewer's thoughtful and constructive feedback, which has significantly contributed to strengthening our work. We hope our response sufficiently addresses the concerns raised, and we would be grateful if the reviewer considers increasing the rating. Please feel free to raise any additional questions."}, {"review_id": "kjuCyI7QTj", "reviewer": "Reviewer_nSnG", "summary": "This paper addresses the Federated continual learning (FCL) task, where the key challenges lie in temporal forgetting over time and spatial forgetting. The authors underscore that state-of-the-art prompt-based FCL methods are prone to class-wise knowledge coherence between prompts across clients. The class-wise knowledge coherence includes intra-class distribution gap across clients and inter-prompt class-wise relevance. To address these issues, a novel method, Class-aware Client Knowledge Interaction (C$^2$Prompt), is proposed to enhance class-wise knowledge coherence during prompt communication. Extensive experiments on multiple FCL benchmarks demonstrate the effectiveness of the proposed method.", "questions": "The robustness and generalizability of the method could be better demonstrated by:\n1. Including results on additional benchmarks (e.g., ImageNet-A and CIFAR-100) under federated class-incremental learning (FCIL) settings.\n2. Providing a clearer definition and justification of the Final Average Accuracy (FAA) metric.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength:**\n- The structure of this paper is well-organized with clear writing and well-designed figures that effectively illustrate methodological innovations and superiority.\n- This paper is well-motivated and introduces a novel method to address the FCL problem. The analysis of class-wise knowledge coherence issues is insightful in FCL. The class distribution-guided local distribution compensation and server-side prompt-aggregation designs are innovative and reasonable.\n- The design of LCDC and CPA is well-justified with clear mathematical discussion and theoretical support in Appendix A. The distribution aggregation and compensation mechanism is especially thoughtful.\n- The proposed method achieves state-of-the-art results on multiple FCL benchmarks, particularly in average accuracy and AIA metrics. Besides, the ablation studies and performance curves are informative and verify the effectiveness of each component. Communication and parameter overheads are also thoroughly analyzed. \n\n**Weakness:**\n- Limited FCIL Benchmark Diversity: Under the federated class-incremental learning setting, only ImageNet-R results are provided. Expanding comparisons to ImageNet-A and CIFAR-100 would better demonstrate the generalizability of the proposed method.\n- Undefined Metric: Final Average Accuracy (FAA) metric in the FCIL setting requires explicit definition and differentiation from related metrics (Avg, AIA, FM, FT, BT, CT).", "was_revised": true, "final_justification": "Thank the authors for their thorough rebuttal. My previous concerns have been satisfactorily addressed by the additional experiments and detailed discussion provided. Furthermore, after reviewing the feedback from the other reviewers and the authors' responses, I believe this work is highly meaningful and provides valuable insights to the federated continual learning community. Accordingly, I am raising my score from 4 to 5.", "rebuttal": "We sincerely appreciate the reviewer's constructive feedback and recognition. Please find our point-by-point responses below.\n\n**W1: More experimental comparison on FCIL benchmarks**\n\nThank you for the valuable suggestion. We have conducted additional experiments on CIFAR-100 and ImageNet-A under the Federated Continual Incremental Learning (FCIL) setting [a], using the Dirichlet distribution with parameter $\\beta$ to control data heterogeneity. The results are presented below:\n\n|CIFAR-100|$\\beta$=0.5|$\\beta$=0.1|$\\beta$=0.05|\n|-|-|-|-|\n|LoRM|86.95|81.76|82.03\n|Powder|87.46|85.33|82.76\n|Ours|89.93|87.67|83.25\n\nOur method outperforms the state-of-the-art FCIL method LoRM by **2.98%/5.91%/1.22%** at $\\beta=0.5 / 0.1 / 0.05$, respectively. Compared to the prompt-based FCL baseline Powder, we achieve **2.47%/2.34%/0.49%** improvements under the same settings.\n\n\n|ImageNet-A|$\\beta$=0.5|$\\beta$=0.1|$\\beta$=0.05|\n|-|-|-|-|\n|LoRM|37.26|36.34|33.11\n|Powder|39.29|37.11|35.62\n|Ours|40.51|39.76|37.21\n\nSimilarly, our method surpasses LoRM by **3.25%/3.42%/4.10%**, and Powder by **1.22%/2.65%/1.59%** at $\\beta=0.5 / 0.1 / 0.05$, respectively.\n\nIn summary, these results validate the robustness and adaptability of our method under varying levels of data heterogeneity. In particular, the growing performance gains under smaller $\\beta$ values can be attributed to our inter-client, intra-class distribution compensation mechanism, which enhances the model's ability to acquire and aggregate discriminative knowledge while mitigating inter-client conflicts.\n\n[a] Closed-form merging of parameter-efficient modules for federated continual learning. ICLR 2025\n\n**W2: Definition of FAA**\n\nThanks for pointing this out.\n\n(1) Final Average Accuracy (FAA) is a standard metric used in FCIL to measure knowledge retention and accumulation. Let $a^t$ denote the test accuracy on the $t$-th task after the final incremental step. FAA is defined as:\n$$\\mathrm{FAA}=\\frac{1}{T}\\sum_{t=1}^Ta^t$$\nwhere $T$ is the total number of tasks. A higher FAA indicates better overall performance across all tasks and stronger continual learning ability.\n\n(2) We have incorporated the definition of FAA into our appendix to improve the clarity and comprehensiveness of our paper.\n\nWe hope our responses adequately address your concerns. Please feel free to reach out with any further questions or suggestions."}, {"review_id": "E5dlrZ1eU5", "reviewer": "Reviewer_Zi11", "summary": "This paper proposes C²Prompt, a method for federated continual learning (FCL) that enhances class-wise knowledge coherence across distributed clients to mitigate both temporal and spatial forgetting. The key insight is that prompt-based FCL suffers from intra-class distribution gaps and inter-class knowledge confusion during prompt communication. To address this, the authors introduce two core components: a local class distribution compensation (LCDC) mechanism to align intra-class semantics across clients, and a class-aware prompt aggregation (CPA) strategy to preserve discriminative, class-relevant knowledge. By explicitly modeling and resolving class-level inconsistencies, C²Prompt enables more robust and coherent learning across clients. Experimental results on standard FCL benchmarks show that the proposed method achieves state-of-the-art performance.", "questions": "1. Can the authors provide additional qualitative or quantitative evidence to directly support the claim that C²Prompt improves both intra-class distribution alignment and inter-prompt class-wise relevance?\n\n2. What is the specific contribution of the knowledge distillation (KD) loss (from Powder [19]) to the overall performance? Could you provide an ablation that excludes KD to isolate the gains from LCDC and CPA?\n\n3. Why is the local class distribution compensation (LCDC) prompt retrieved using the label y, rather than being generated through the instance-specific weighted combination strategy used for the local discriminativity prompts?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The paper presents a well-motivated and thoughtfully designed prompt-based approach for FCL, with a clear emphasis on improving class-wise knowledge coherence. The proposed components (LCDC and CPA) are effectively integrated, leading to strong empirical performance that surpasses recent competitive baselines.\n\n2. The paper is well-written and easy to follow.\n\nWeaknesses\n1. While the method is shown to outperform prior approaches, the claim that C²Prompt improves both inter-prompt class-wise relevance and intra-class distribution alignment would be stronger with more direct qualitative or quantitative evidence. For example, visualizations (e.g., t-SNE plots) or class-wise similarity metrics could help demonstrate the improvements in class coherence more explicitly.\n\n2. In addition to the proposed LCDC and CPA components, the method incorporates the knowledge distillation (KD) loss introduced by Powder [19]. However, the paper lacks an ablation study isolating the impact of the KD loss. As a result, it remains unclear how much of the performance gain over baselines (especially beyond Powder) stems from the new components versus the KD loss. A more detailed ablation would significantly strengthen the empirical validation.\n\n3. The mechanism by which the local class distribution compensation prompt is retrieved from the pool using the label y warrants further justification. Specifically, why is it not generated via the same instance-specific weighted-sum strategy used for the local discriminativity prompts? Clarifying this design choice could help readers better understand the rationale and benefits of using explicit label-based indexing in this context.\n\n4. Typo: “the ocal class distribution” -> “the local class distribution”.", "was_revised": true, "final_justification": "Thanks for the detailed rebuttal, which has addressed most of my concerns. Given these clarifications and the overall strength of the proposed method, I will maintain my recommendation, now with increased confidence in the paper’s contributions.", "rebuttal": "Thank you for your constructive feedback and recognition. Below are our responses, which we hope effectively address your concerns.\n\n\n**W1: Improvements in class coherence**\n\nWe appreciate your insightful suggestion. As visualizations are not supported during the rebuttal phase, we instead provide quantitative evidence to demonstrate how our method enhances inter-prompt class-wise relevance and intra-class distribution alignment.\n\n(1) Mean Inter-Prompt Class-wise Relevance Score (mPR).\n\nThe mPR measures the overall class-wise relevance among prompts during fusion. It is defined as:\n$$mPR=1/N_p\\sum_{i=1}^{N_p}(\\sum_{j=1}^C\\sum_{k=1}^{N_p}s_{i,j}W_{i,k}s_{k,j})$$\nwhere $N_p$ and $C$ are the prompt number and class number in a task, respectively. $s_{i,j}$ is the proportion of class $j$'s matched instance for prompt $i$. $W_{i,k}$ denotes the weight of prompt $k$ when generating the fusion prompt corresponding to prompt $i$.\n\nThe mPR comparison of our method and the state-of-the-art Powder is shown as follows:\n\n|Method|Powder|Ours|\n|-|-|-|\n|mPR↑|0.124|0.160|\n\nOur method achieves a **0.036** absolute gain in mPR, corresponding to a **29.0%** relative improvement over Powder. This result verifies that our Class-aware Prompt Aggregation design effectively filters and fuses class-relevant prompts, leading to better semantic alignment. As a result, our method achieves superior new knowledge acquisition and anti-forgetting capacity, as shown in Figure 1(a) of the main paper.\n\n(2) Mean Intra-Class Distribution Distance (mCD).\n\nmCD assesses the distributional alignment between local and global class representations. It is calculated as:\n\n$$mCD=\\frac{1}{KC}\\sum_{k=1}^{K}\\sum_{j=1}^CKL(d_j, d_{k,j})$$\nwhere $K$ is the client number, $ d_i$ is the ground truth distribution of class $j$, which is a Gaussian distribution obtained by calculating the mean and variance of the instance features of class $j$. $d_{k,j}$ is the local distribution of class $j$ on client $k$. $d_{k,j}$ is also a Gaussian distribution. For existing methods, $d_{k,j}$ is obtained by calculating the mean and variance of class $j$ using data of client $k$. In our method, $d_{k,j}$ is a fused distribution of the class-wise local data and transferred data.\n\nThe quantitative comparison of our method and Powder under the mCD metric is reported as follows:\n\n|Method|Powder|Ours|\n|-|-|-|\n|mCD↓|7.67|6.98|\n\nOur method reduces mCD by **0.69** compared to Powder, which is a **9%** improvement, demonstrating that the proposed Local Class Distribution Compensation module effectively narrows the gap between local and global distributions. This promotes inter-client knowledge consistency and enhances the model's overall knowledge accumulation, as shown in Figure 1(b) of our main paper.\n\n\n**W2: Ablation on the KD loss**\n\n(1) The KD loss is an inherent component of the Powder baseline. Therefore, the performance improvement reported in our experiments is solely attributed to our proposed LCDC and CPA modules. Specifically, as illustrated in Figure 4 of our main paper, LCDC and CPA individually contribute **1.88%** and **1.33%** improvements, respectively. When combined, a total gain of **2.51%** is observed.\n\n(2) To further validate the effectiveness of our proposed modules, we conduct experiments by removing the KD loss used in Powder. The results are presented below:\n \n |Method|Powder-w/o KD|Ours-w/o KD|\n|-|-|-|\n|Avg|77.63|79.22|\n|AIA|76.01|76.62|\n\nEven without KD, our method consistently outperforms the Powder baseline, achieving **1.59%** on Avg and **0.61%** on AIA. These results further confirm the robustness of our design, independent of KD.\n\n(3) In fact, KD is a **widely adopted fundamental technique** in federated continual learning methods, such as Podnet [a], CFeD [b], GLFC [c], FedSpace [d], and Powder [e]. It is typically employed to align the aggregated global model with local client models, especially under non-IID task distributions. Without KD, local models tend to overfit to new data and forget previously learned global knowledge. Therefore, the use of KD is orthogonal and complementary to our contributions.\n\n[a] Podnet: Pooled Outputs Distillation for Small-Tasks Incremental Learning. ECCV 2020\n\n[b] Continual Federated Learning based on Knowledge Distillation. IJCAI 2022\n\n[c] Federated Class-Incremental Learning. CVPR 2022\n\n[d] Asynchronous Federated Continual Learning. CVPR 2023\n\n[e] Federated Continual Learning via Prompt-based Dual Knowledge Transfer. ICML 2024\n\n\n**W3: The design choice of local class distribution compensation prompt**\n\n(1) We retrieve the local class distribution compensation prompt from the pool using the label $y$ to **avoid inter-class interference** during prompt training.\n\n(i) Specifically, local distributions for each class typically exhibit **independent and random shifts** from the global distribution. As a result, there are no unified semantic patterns across classes for guiding distribution-shift modeling. Using a shared prompt pool for all classes, followed by weighted fusion, introduces **optimization conflicts** across classes and leads to sub-optimal learning.\n\n(ii) In contrast, our method explicitly utilizes the class label $y$ to segregate prompts for different classes, enabling **focused and optimal learning** of class-specific prompts.\n\n\n(2) We further compare our method with a weighted-sum prompt strategy. The results are as follows:\n\n |Method|weighted-sum|Ours|\n|-|-|-|\n|Avg|86.30|87.20|\n|AIA|85.72|85.93|\n\nOur approach surpasses the weighted-sum baseline by **0.90%** on Avg and **0.21%** on AIA, confirming the effectiveness of our class-specific compensation prompt design in capturing localized distribution shifts in federated scenarios.\n\n\n**W4: Typo**\n\nThank you for identifying this issue. We have corrected \"the ocal class distribution\" to \"the local class distribution\" in the revised version.\n\nWe sincerely appreciate your thoughtful and constructive feedback, which has significantly contributed to strengthening our work. We hope our response sufficiently addresses the concerns raised, and we would be grateful if the reviewer considers increasing the rating. Please feel free to raise any additional questions."}]

Author rebuttal:

Meta-review: This paper proposes C²Prompt, a novel method for Federated Continual Learning that addresses class-wise knowledge coherence via two key modules: Local Class Distribution Compensation (LCDC) and Class-aware Prompt Aggregation (CPA). The approach is well-motivated, clearly presented, and technically sound, with comprehensive experiments demonstrating consistent state-of-the-art performance across multiple datasets, model scales, and cross-dataset scenarios.

The authors’ rebuttal successfully addressed reviewers’ concerns, adding new experiments, clarifying metrics, and providing strong evidence of robustness under severe data heterogeneity. Given its novelty, solid empirical results, and practical relevance, this paper makes a meaningful contribution to the FCL community.
Decision: Accept (poster)