Title: C$^2$Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Continual Learning; Federated learning
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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”.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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).\n\nQUESTIONS:\nThe 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-17"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper identifies two underexplored but crucial sources of knowledge degradation in prompt-based FCL: intra-class distribution gaps and inter-prompt class relevance.
2. The proposed method outperforms several state-of-the-art approaches across six evaluation metrics.
3. The paper is well written and easy to follow.
4. Theoretical justification is provided to further demonstrate the usefulness of the designs in the proposed paper.

Weaknesses:
1. More datasets are expected for a comprehensive comparison.
2. The efficiency and communication costs of the proposed method (compared to baselines) can be further discussed.
3. The experiments on scenarios with severe data heterogeneity are expected.

QUESTIONS:
The authors are expected to address the concerns in the "weaknesses".

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1.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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-28"}