Title: Class-wise Balancing Data Replay for Federated Class-Incremental Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Federated Learning;Federated Class-Incremental Learning; Data Replay
Abstract: Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay has become a promising solution, which can alleviate forgetting by reintroducing representative samples from previous tasks. However, their performance is typically limited by class imbalance, both within the replay buffer due to limited global awareness and between replayed and newly arrived classes. To address this issue, we propose a class-wise balancing data replay method for FCIL (FedCBDR), which employs a global coordination mechanism for class-level memory construction and reweights the learning objective to alleviate the aforementioned imbalances. Specifically, FedCBDR has two key components: 1) the global-perspective data replay module reconstructs global representations of prior task knowledge in a privacy-preserving manner, which then guides a class-aware and importance-sensitive sampling strategy to achieve balanced replay; 2) Subsequently, to handle class imbalance across tasks, the task-aware temperature scaling module adaptively adjusts the temperature of logits at both class and instance levels based on task dynamics, which reduces the model’s overconfidence in majority classes while enhancing its sensitivity to minority classes. Experimental results verified that FedCBDR achieves balanced class-wise sampling under heterogeneous data distributions and improves generalization under task imbalance between earlier and recent tasks, yielding a 2%-15% Top-1 accuracy improvement over six state-of-the-art methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FedCBDR, a new method to address catastrophic forgetting in federated class-incremental learning (FCIL) by explicitly tackling class imbalance issues. The authors identify two sources of imbalance in replay-based FCIL: (i) imbalance within the replay buffer due to clients selecting examples without global awareness, and (ii) imbalance between replayed old classes and newly introduced classes in incremental tasks. To mitigate these, FedCBDR introduces two key components. First, a global-perspective data replay (GDR) module constructs a pseudo global representation of prior tasks in a privacy-preserving way and uses it to guide class-balanced exemplar selection across clients. Second, a task-aware temperature scaling (TTS) module dynamically calibrates prediction logits (with task-specific temperature adjustment and instance-level weighting) to reduce overconfidence in majority classes while boosting minority classes. The method allows each client to incorporate a globally informed, class-balanced replay selection and applies confidence scaling to handle long-tail class distributions over time. Experimental results show that FedCBDR yields significantly improved retention of old classes and overall accuracy compared to existing methods. Evaluations on three benchmark image classification datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet) under various non-i.i.d. settings demonstrate 2%–15% higher Top-1 accuracy than six state-of-the-art FCIL approaches.\n\nSTRENGTHS AND WEAKNESSES:\n**Quality Strengths:**\n\nThe technical approach of FedCBDR appears valid. The two primary components, GDR and TTS, are motivated in the context of the class imbalance problem prevalent in FCIL. While the application of SVD for identifying globally salient features and the use of temperature scaling for calibrating the learning objective is well-established machine learning techniques, they are in this paper combined and applied to the specific challenges of the FCIL setting. \n\n**Quality Weaknesses:** \n\n#1. The claim that the GDR module is \"privacy-preserving\" is not formally substantiated with a rigorous privacy analysis. While raw data samples are not directly shared with the server, the ISVD process involves a client-specific matrix $P_k^{(i)}$ and a globally shared matrix $Q^{(i)}$ in the transformation $X_k^{(i)'}=P_k^{(i)} X_k^{(i)} Q^{(i)}$. The subsequent aggregation of these transformed features $X_k^{(i)'}$ at the server, followed by SVD, might still be vulnerable to various inference attacks or information leakage about the original client data. The paper cites references in the context of SVD, which pertain to \"efficient decentralized federated singular vector decomposition\" and \"practical lossless federated singular vector decomposition\" respectively. However, the specific privacy properties of these methods and a clear explanation of how they apply to the FedCBDR framework to ensure privacy are not adequately detailed. We invite the authors to present a formal and rigorous data privacy analysis (e.g., differential privacy guarantees). \n\n#2. For the experiments, the absence of GLFC (Global-Local Forgetting Compensation, NeurIPS 2023) or its contemporary equivalents is critical, though they are discussed in related work. Given that GLFC specifically addresses forgetting in FCIL with a form of global coordination, it would be a natural baseline to include. We invite the authors to present a comparison with it. Similarly, one might wonder about simple baselines like FedAvg + replay buffer with uniform sampling (without FedCBDR's balancing) as a point of reference – although the paper does compare to Re-Fed, which is a strong replay-based method, an even simpler baseline could show the raw effect of class imbalance.\n\n**Clarity Strengths:**\n\nIn general, the paper is well-written and structured. The problem formulation and related work give a clear background on FCIL and existing replay strategies. The method is illustrated with a framework diagram (Figure 2) and detailed step-by-step description, which aids understanding of the GDR and TTS modules.\n\n**Clarity Weaknesses:**\n\n#1. The description of the SVD/ISVD process within the GDR module could be more precise. Labeling the transformation $X_k^{(i)'}=P_k^{(i)} X_k^{(i)} Q^{(i)}$ as an \"encryption\" might be misleading if not used in a formal cryptographic sense. Further clarification on this transformation (e.g., feature obfuscation or rotation for privacy) and more details on the generation, properties (e.g., orthogonality), and secure sharing mechanism for the matrices $P_k^{(i)}$ and $Q^{(i)}$ are necessary. \n\n#2. In addition, we invite the authors to clarify the dimensions of the matrix $\\sum^{(i)'}$ resulting from the SVD operation. \n\n#3. Besides, there appears to be a mismatch or mislabeling concerning Figure 7 and Figure 8 in the appendix. The captions for these two figures appear to be identical, while the figures themselves depict different types of information (performance curves versus sample distributions).\n\n**Significance Strengths:**\n\nThe paper addresses a challenge in federated class-incremental learning – the class imbalance in replay data – which is often overlooked in prior work. By articulating the issue (both local replay bias and long-tailed new vs. old class distribution), the authors establish a motivation for FedCBDR. The proposed solution directly targets this gap.\n\n**Significance Weaknesses:**\n \n#1. Scalability concerns may limit the perceived significance of large-scale, real-world FL deployments. The experiments are conducted with a relatively small number of clients ($K=5$ in the main paper, $K=10$ in the appendix). The performance, computational feasibility (especially the server-side SVD in GDR), and communication efficiency of FedCBDR on much larger federated networks, potentially involving hundreds or thousands of clients, are not demonstrated.\n\n#2. The sensitivity of the TTS module to its hyperparameters ($\\tau_{old}, \\tau_{new}, w_{old}, w_{new}$) is a practical concern. Figure 4 illustrates that performance can vary considerably with different hyperparameter settings, and inappropriate choices can be detrimental. If extensive and dataset-specific tuning is required for these hyperparameters in each new scenario, it reduces the method's ease of use and practical significance. Indeed, the authors also acknowledge this by proposing future work to develop methods with \"less sensitivity to hyperparameters\".\n\n**Originality Strengths:**\n\nThe idea of incorporating a global perspective into replay buffer construction seems to be new in the FCIL context. Instead of each client selecting exemplars myopically, FedCBDR's GDR module uses a server-side aggregation of pseudo-features (via SVD-based feature decomposition) to guide class-balanced sampling across clients. This mechanism appears to be a novel combination of techniques (singular value decomposition for representative selection, leverage score sampling, etc.) not seen in prior FCIL methods. Similarly, the TTS module introduces a multi-level logit calibration scheme for federated learning, which seems to be an original way to handle class imbalance between tasks.\n\n**Originality Weaknesses:**\n \n#1. The method builds upon known techniques – exemplar replay and temperature scaling – by combining them with a global feature aggregation step. Some elements have precedents: for example, prior work GLFC (referenced as [9]) also introduced global coordination (via gradient compensation and prototype sharing) to address forgetting. \n\n#2. FedCBDR's global pseudo-feature approach is different, but the authors could better clarify how their method fundamentally advances beyond such earlier approaches. \n\n#3. The temperature scaling idea for class imbalance, while effective, is a relatively straightforward adaptation of calibration methods to this setting.\n\nQUESTIONS:\n#1. Pseudo-Feature Construction: The mechanism for constructing and using pseudo global features in the GDR module could be described more clearly. Currently, it's mentioned that each client performs an inverse SVD (ISVD) on local features to produce pseudo-features, and then the server aggregates and applies SVD to select representative samples. This is a complex procedure – for instance, what exactly are the \"pseudo-features\" mathematically, and how is inverse SVD used? It would help if the paper included a concise algorithm or a toy example illustrating this process step-by-step. In particular, formally and rigorously analyzing how privacy is preserved (e.g. are the random orthogonal bases used in ISVD shared or kept secret? Can the server reconstruct original data from the pseudo-features?) is essential.\n\n#2. Comparison to GLFC or Related Methods: As noted in the weaknesses, GLFC is a closely related prior work tackling a similar problem. We invite the authors to clarify how FedCBDR differs from and improves upon GLFC (and any other prior FCIL methods with global coordination). Is the key difference that FedCBDR achieves true class-balanced replay, whereas GLFC focuses on gradient alignment? If so, emphasizing this distinction in the introduction or related work would sharpen the paper's contribution. Adding a direct experimental comparison would be necessary.\n\n#3. TTS Hyperparameter Sensitivity & Selection: The Task-aware Temperature Scaling (TTS) module's performance is shown to be sensitive to the choice of its hyperparameters: τ_old,τ_new,w_old,w_new (as indicated in Figure 4 ). (i) The paper provides specific tuning ranges used in the experiments (e.g., τ_old∈{0.8,0.9}). How were these particular ranges determined? Was it based on prior work, preliminary experiments, or other heuristics? (ii) Is there a more principled approach or a robust heuristic for setting these hyperparameters that goes beyond dataset-specific grid search? For instance, could these parameters be adapted based on observable task statistics, such as class imbalance ratios or model confidence levels on old versus new tasks?\n\n#4. Computational Cost & Scalability of GDR: The GDR module involves client-side ISVD and server-side SVD operations, as well as communication of features and scores. What is the theoretical computational complexity of these operations? Furthermore, how much data (in terms of volume, e.g., bytes) needs to be communicated per round or per task between the clients and the server (e.g., for pseudo-features, leverage scores, selected sample indices)? Crucially, how do these computational and communication costs scale with key parameters such as the number of clients (K), the number of samples per client, the dimensionality of the features (d), and the total number of tasks? We invite the authors to provide a theoretical complexity analysis for the main computational steps in GDR. Additionally, providing empirical measurements (e.g., wall-clock time for computations, actual data volume transmitted) from the experimental setup would be highly beneficial.\n\n#5. Realistic Federated Settings: The authors are encouraged to discuss how FedCBDR might extend to more realistic FL scenarios. For example, what if the number of clients is very large or if clients drop in and out (asynchronous participation)? Does the method require any synchronization (e.g. all clients must send features at the end of each task) that could be problematic if some clients are slow or offline?\n\nLIMITATIONS:\n**Author-Acknowledged Limitations:**\n\n#1. Feature Transmission Costs in FedCBDR: The authors state an intention to \"investigate lightweight sampling strategies to reduce feature transmission costs in FedCBDR\". This implicitly acknowledges that the current GDR module, which involves clients sending transformed features $X_k^{(i)'}$ to the server, may incur significant communication overhead. To better motivate this future work and provide context, it would be beneficial to quantify the current feature transmission costs.\n\n#2. Hyperparameter Sensitivity of Balancing Methods (TTS): The plan to \"develop more robust post-sampling balancing methods that mitigate class imbalance with less sensitivity to hyperparameters\"  directly addresses the observation that the TTS module's effectiveness can depend on careful tuning (Figure 4). \n\n#3. Sampling for Globally Imbalanced Distributions: The authors identify that \"designing sampling strategies for globally imbalanced distributions remains an open problem\". It would be helpful to clarify how this \"globally imbalanced distribution\" problem differs from the class imbalance issues that FedCBDR currently aims to tackle (i.e., imbalance within replay buffers and between replayed and new task samples). If this refers to a more extreme or persistent form of imbalance that affects all tasks and clients from the outset, it would be useful to frame the scope of the current work more precisely against this larger, acknowledged challenge.\n\n**Missing or Under-Discussed Limitations:**\n\n#1. Depth of Privacy Guarantees for GDR: The assertion that GDR is \"privacy-preserving\" is not sufficiently substantiated with rigorous analysis. This stands as a critical limitation for a method proposed for federated learning, where privacy is a cornerstone. The authors should undertake a formal privacy analysis of the GDR module (e.g., within the differential privacy framework or by analyzing resistance to specific attack models). \n\n#2. Server-Side Computational Bottleneck for GDR: The SVD operation performed on the aggregated pseudo-feature matrix $X_k^{(i)'}$ on the server can become a computational bottleneck, particularly as the number of participating clients, the number of samples per client, or the dimensionality of the features increases. This potential limitation should be explicitly discussed. The authors could suggest potential mitigation strategies for future work, such as the use of approximate SVD methods, incremental SVD, or randomized SVD techniques that might offer a better trade-off between accuracy and computational cost. Defining the scale (in terms of number of clients, total samples, feature dimensions) at which FedCBDR is currently practical based on their experimental setup would also be informative.\n\n#3. Generality of Datasets: The experiments are focused on image classification benchmarks with relatively small images. It remains to be seen if the approach would scale to more complex data (e.g. high-resolution images like ImageNet-1k). We invite the authors to evaluate FedCBDR on ImageNet-1k dataset.\n\n#4. Assumption of Synchronous Communication: The training pipeline described in Algorithm 1 implies a synchronous federated learning setup, where the server waits for updates (local models and pseudo-features) from all participating clients before proceeding with aggregation and the SVD operation. Real-world federated learning systems often have to contend with asynchronous client participation, device dropouts, or stragglers. We invite the authors to discuss whether the FedCBDR framework can be adapted to asynchronous settings or acknowledge the reliance on synchronous communication as a current limitation.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FedCBDR, a class-wise balancing data replay strategy for Federated Class-Incremental Learning (FCIL). To alleviate the replay-induced class imbalance caused by local client views and task dynamics, FedCBDR introduces two key modules: Global-perspective Data Replay (GDR) and Task-aware Temperature Scaling (TTS). GDR constructs a privacy-preserving global pseudo-feature space to enable representative and balanced sample selection. TTS dynamically calibrates softmax temperature and loss weighting based on task recency to mitigate model overconfidence in majority classes. Extensive experiments demonstrate that FedCBDR consistently outperforms existing methods across multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\ni. The proposed method is both simple and effective, with a technically sound overall design. This work has potential to become a new baseline in federated class-incremental learning (FCIL).\n\nii. The overall flow of the paper is sufficiently clear, and the structure of the manuscript is well-organized.\n\niii. The authors conducted extensive experiments and validated that the proposed FedCBDR outperforms existing methods. They also provided case studies to further illustrate the effectiveness of the approach.\n\nWeaknesses\n\ni. The meaning of n_k^i in lines 136 and 139 may need further clarification. Is it related to defined in line 138? Some descriptions may need correction. For instance, the term “Global-perspective Active Data Replay” in line 208 should be revised to “Global-perspective Data Replay”\n\nii. It is recommended to unify the notation throughout the paper. For example, in Section 3, t is used to denote tasks, while in Section 4, i is used for the same purpose. \n\niii. The paper could better explain the motivation for using leverage scores and how it compares to random or uniform sampling strategies.\n\nQUESTIONS:\ni. Will uploading features pose a risk of privacy leakage?\n\nii. How can the additional communication cost caused by uploading features be reduced? \n\niii. Is the performance of the proposed method sensitive to the order in which tasks are presented?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the key challenges in Federated Class-Incremental Learning (FCIL), namely inter-task class imbalance and catastrophic forgetting. The proposed method, FedCBDR, demonstrates a certain level of innovation in its design, particularly in how it achieves class-balanced replay through global reconstruction under the federated setting. Extensive empirical evaluations on multiple benchmark datasets confirm its strong performance, with the method remaining robust even under highly heterogeneous (non-IID) conditions. Overall, this work provides a practical and effective solution framework for FCIL tasks and shows strong potential to serve as a baseline for future research.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper is well-motivated and tackles a critical problem in Federated Class-Incremental Learning (FCIL), namely the challenge of class imbalance and catastrophic forgetting across tasks.\n2. The proposed FedCBDR method is technically sound and thoughtfully designed. It integrates global feature reconstruction and task-aware temperature scaling in a lightweight and modular manner.\n3. The experimental evaluation is thorough and convincing. It includes multiple datasets, diverse settings, ablation studies, and analysis that clearly demonstrate the effectiveness of each component of the method.\n\n\nMajor Weaknesses\n1. The feature uploading process appears to introduce additional communication overhead.\n2. The authors claim in line 199 that training a generator incurs higher costs than local training. Is there any empirical evidence provided to support this statement?\n\n\nMinor Weaknesses\n1. The bolded content mentioned in the appendix appears to be missing in the actual tables.\n2. The notation used in Figure 2 appears to be inconsistent with that in the main text.\n3. The meaning of Concat() in Equations (7) and (9) has not been clearly defined.\n\nQUESTIONS:\n1. What are the experimental settings for TARGET and LANDER, particularly regarding the number of generated samples?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
The work is about federated class-incremental learning, which considers class imbalance problem and developed FedCBDR which employs a global coordination mechanism for class-level memory construction and reweights the learning objective. It is composed of two key components, i.e., the global-perspective data replay module and the task-aware temperature scaling module, which respectively achieve balanced replay and alleviate the imbalance issue. The authors also conducted a number of experiments to evaluate its superiority.

STRENGTHS AND WEAKNESSES:
**Strength**
1.  The paper is well-structured with clear organization and motivations. The problem to be addressed is well analyzed and formalized. 
2. The solution achieves both efficiency and efficacy, as demonstrated by the experiments.
3. Experiments are sufficient;
4. The authors conduct a wide range of literature reviews, including the latest works.

**Weaknesses** 

1. Some symbols are not clearly defined; it seems that there exists an inconsistent use of a single notation.
2.  Some implementation details are not directly provided, such as the hyperparameters used in the work, the replay buffer size, etc.
3. Some designs require more detailed explanation. For example, the process of Pseudo Feature Construction uses global model.

QUESTIONS:
Refer to the weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-02