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FedRKMGC: Towards High-Performance Gradient Correction-based Federated Learning via Relaxation and Fast KM Iteration |
Soundness: 3: good
Presentation: 3: good
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper proposes FedRKMGC, a novel federated learning (FL) framework that integrates gradient correction with the fast KM acceleration method and global relaxation technique. It aims to address the problems of slow convergence and client drift in FL under heterogeneous data distributions. The key contributions include: 1) a unified framework that combines gradient correction with fixed-point acceleration to enhance both stability and convergence speed; 2) a two-level acceleration mechanism, with fast KM extrapolation for client-side local updates and global relaxation for server-side aggregation; 3) extensive experiments on CIFAR-10 and CIFAR-100 datasets, demonstrating that FedRKMGC outperforms state-of-the-art FL methods in convergence speed, final accuracy, and communication efficiency.
- Originality: The first to combine fast KM acceleration and global relaxation into a unified FL framework.
- Technical depth: Solid grounding in convex optimization and operator theory, connecting FL to fixed-point iteration literature.
- Empirical validation: Extensive experiments on CIFAR-10/100 with multiple non-IID settings, ablations, sensitivity studies, and robustness tests.
- Significance: Improves both stability (drift reduction) and communication efficiency—a central issue in FL.
- Clarity: Strong writing quality, comprehensive experimental section, and thoughtful discussion on future theoretical analysis.
- Insufficient theoretical analysis: The paper fails to provide a formal theoretical proof of the convergence rate. Although it mentions that fast KM can accelerate convergence from $O(1/\sqrt{T})$ to $O(1/T)$ for fixed-point problems, it does not extend this to the federated learning scenario, leaving the theoretical validity of FedRKMGC incompletely justified.
- Limited hyperparameter guidance: While the paper reports hyperparameter values used in experiments, it lacks a systematic strategy for hyperparameter selection. The sensitivity analysis shows that the correction parameter \(\beta\) significantly impacts performance, but no method is proposed to optimize its value adaptively.
- Narrow dataset coverage: Experiments are only conducted on image classification datasets (CIFAR-10/100). The performance of FedRKMGC on other types of data (e.g., text, tabular) or more complex FL scenarios (e.g., model heterogeneity, non-convex objectives) is untested, limiting the generalizability of the results.
1. Can the authors quantify the computational overhead of fast KM extrapolation at each client compared to FedDyn or SCAFFOLD?
2. How sensitive is the performance to incorrect tuning of $\gamma$ or $\rho$ beyond the reported ranges? Could adaptive or learnable schemes for these hyperparameters further improve stability?
3. Have the authors explored the applicability to non-vision tasks, e.g., language or sensor data, to test generality?
4. Would it be possible to derive a partial convergence guarantee (e.g., for convex objectives or bounded variance assumptions) to strengthen the theoretical contribution? |
Fully AI-generated |
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FedRKMGC: Towards High-Performance Gradient Correction-based Federated Learning via Relaxation and Fast KM Iteration |
Soundness: 2: fair
Presentation: 2: fair
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper introduces FedRKMGC, a federated learning algorithm that addresses the dual challenges of slow convergence and client drift in heterogeneous data settings. The method integrates three key components: (1) gradient correction to mitigate client drift, (2) fast Krasnosel'skiĭ–Mann (KM) iteration for local acceleration, and (3) global relaxation for server-side acceleration.
1. The paper presents an interesting combination of classical optimization techniques (fast KM iteration and relaxation) applied to federated learning.
2. FedRKMGC consistently outperforms baselines across all settings, with particularly impressive gains on CIFAR-100.
3. The method demonstrates significant communication savings, requiring roughly half the rounds of competing methods to reach target accuracy thresholds.
1. While the authors acknowledge this limitation and provide some discussion in the appendix, the absence of convergence guarantees is a significant weakness for a traditional FL paper targeted on the top conference like ICLR.
2. Only image classification tasks (CIFAR-10/100) are evaluated. And no comparison with more recent acceleration methods in FL.
3. While the authors claim robustness to relaxation (ρ) and KM (γ) parameters, the gradient correction parameter (β) appears quite sensitive based on Figure 4(a).
4. The paper doesn't analyze the additional computational cost of the fast KM iteration and correction vector maintenance at the client side, which could be important for resource-constrained devices.
5. Some notation is introduced without clear definition.
1. The relationship between the "raw correction" and "fast KM correction" in Algorithm 1 (lines 11-12) needs better motivation. Why is this specific form of extrapolation chosen?
2. The paper mentions that FedADMM is trained with the same number of local epochs "for fairness," but this may not be the optimal configuration for that method.
3. No comparison with standard KM iteration (without the "fast" variant) to quantify the specific benefit of the fast KM acceleration. |
Fully AI-generated |
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FedRKMGC: Towards High-Performance Gradient Correction-based Federated Learning via Relaxation and Fast KM Iteration |
Soundness: 2: fair
Presentation: 3: good
Contribution: 2: fair
Rating: 6: marginally above the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
The paper introduces FedRKMGC, a federated learning framework combining gradient correction, fast KM acceleration, and global relaxation to improve convergence and communication efficiency under data heterogeneity. Experiments on CIFAR-10/100 show faster convergence and higher accuracy than state-of-the-art FL methods.
The paper presents a creative idea by integrating fast KM acceleration and relaxation into federated learning, showing moderate improvements in convergence and communication efficiency. While not groundbreaking, the approach is well-motivated, and experiments on standard benchmarks demonstrate consistent, if modest, gains over existing methods.
1. FedRKMGC introduces relation kernelized multi-graph collaboration with KM-based acceleration for federated optimization under non-IID settings. The concept is interesting but closely related to SCAFFOLD (ICML 2020), FedDyn (ICLR 2021), and FedADMM (TPAMI 2023). Including recent methods such as FedU² (CVPR 2024) [1] would clarify novelty.
2. The related work section omits recent multimodal and representation-based FL methods like FedRep [2] and FedU². Broader comparisons would strengthen the positioning.
3. The experimental results are promising but require more details on non-IID splits, client counts, and communication rounds.
4. Add experimental results comparing FedRKMGC with FedDyn, FedRep, and FedU² under identical conditions (e.g., Dirichlet α = 0.1, 0.2, 0.5 with non-IID data splits). Highlight the performance stability and convergence benefits of FedRKMGC, especially under high data heterogeneity.
5. Ablation is limited. Independent evaluation of kernelization, KM acceleration, and relaxation parameters would clarify their contributions.
6. Include an ablation study isolating the RKM module to demonstrate its specific contribution. Analyze inter-client feature alignment (e.g., cosine similarity before and after aggregation) and present
[1] Liao, X., Liu, W., Chen, C., Zhou, P., Yu, F., Zhu, H., Yao, B., Wang, T., Zheng, X., & Tan, Y. (2024). Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data (FedU²). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), pp. 25189–25198.
[2] Collins, L., Hassani, H., Mokhtari, A., & Shakkottai, S. (2021). Exploiting Shared Representations for Personalized Federated Learning. In Proceedings of the 38th International Conference on Machine Learning (ICML 2021), PMLR, pp. 2089–2099.
1. The experimental results are promising but require more details on non-IID splits, client counts, and communication rounds.
2. Add experimental results comparing FedRKMGC with FedDyn, FedRep, and FedU² under identical conditions (e.g., Dirichlet α = 0.1, 0.2, 0.5 with non-IID data splits). Highlight the performance stability and convergence benefits of FedRKMGC, especially under high data heterogeneity.
3. Ablation is limited. Independent evaluation of kernelization, KM acceleration, and relaxation parameters would clarify their contributions.
4. Include an ablation study isolating the RKM module to demonstrate its specific contribution. Analyze inter-client feature alignment (e.g., cosine similarity before and after aggregation) and present. |
Fully AI-generated |