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Federated Averaging convergence analyzed without BGD assumption

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This paper analyzes the convergence of Federated Averaging (FedAvg) with partial participation under a general variance condition. The authors establish a finite-time stationarity bound for smooth nonconvex objectives, which shows a linear speedup with the number of sampled clients. Their results do not require additional data heterogeneity assumptions like bounded gradient dissimilarity (BGD), and they demonstrate its importance with an example that violates BGD but is covered by their analysis. AI

IMPACT Provides theoretical advancements for distributed machine learning optimization techniques.

RANK_REASON Academic paper on optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Federated Averaging convergence analyzed without BGD assumption

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Academic paper on optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ziming Wang, Kun Huang, Karl Henrik Johansson, Shi Pu ·

    On Federated Averaging with Partial Participation under a General Variance Condition

    arXiv:2611.14239v1 Announce Type: cross Abstract: In this paper, we study the convergence of Federated Averaging (FedAvg) with partial participation under a general variance condition. In each communication round, the server samples $S$ of the $n$ clients uniformly at random with…

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