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[2505.11304] Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
Summary
This paper presents a novel approach to federated learning by addressing the challenges posed by heterogeneous client capabilities. The proposed Federated Heterogeneity-Aware Client Sampling (FedACS) method enhances optimization efficiency and reduces inconsistencies.
Why It Matters
As federated learning becomes increasingly prevalent, understanding and mitigating the effects of client heterogeneity is crucial for developing robust AI systems. This research provides a theoretical foundation and practical solution to improve model convergence and efficiency in diverse environments.
Key Takeaways
- Introduces FedACS, a method for optimal client sampling in federated learning.
- Demonstrates that FedACS can significantly reduce communication costs by up to 89%.
- Proves convergence to the correct optimum at a rate of O(1/sqrt(R)).
- Addresses the joint effects of communication and computation heterogeneity.
- Outperforms existing methods by 4.3%-36% in various datasets.
Computer Science > Machine Learning arXiv:2505.11304 (cs) [Submitted on 16 May 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning Authors:Shudi Weng, Chao Ren, Ming Xiao, Mikael Skoglund View a PDF of the paper titled Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning, by Shudi Weng and 3 other authors View PDF Abstract:Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significantly distort the optimization dynamics and lead to objective inconsistency, where the global model converges to an incorrect stationary point potentially far from the pursued optimum. Despite its critical impact, the joint effect of communication and computation heterogeneity has remained largely unexplored, due to the intrinsic complexity of their interaction. In this paper, we reveal the fundamentally distinct mechanisms through which heterogeneous communication and computation drive inconsistency in FL. To the best of our knowledge, this is the first unified theoretical analysis of general heterogeneous FL, offering a principled understanding of how these two forms of heterogeneity jointly distort the optimization trajectory under arbitrary choices of local solvers. Motivated by these insights, we propose Federated Heterogeneity-Aware Client Sampling, FedACS, a universal method to eliminate all t...