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Federated Optimization: Edge of Stability Hinders SCAFFOLD Algorithm

By PulseAugur Editorial · [1 sources] ·

A new research paper explores why the SCAFFOLD algorithm, despite strong theoretical guarantees in federated optimization, often underperforms the simpler FedAvg method in practice. The study proposes that the 'Edge of Stability' (EoS) dynamics, exacerbated by data heterogeneity, significantly degrade SCAFFOLD's ability to accurately estimate the global gradient. This degradation is observed to be inversely proportional to the learning rate and affected by data heterogeneity, suggesting a key limitation for SCAFFOLD in deep learning applications. AI

IMPACT Identifies a key limitation in federated learning algorithms, potentially guiding future research towards more robust optimization methods.

RANK_REASON Research paper published on arXiv detailing a novel finding about federated 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 Optimization: Edge of Stability Hinders SCAFFOLD Algorithm

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Research paper published on arXiv detailing a novel finding about federated 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) · Anant Khandelwal, Michael Crawshaw, Mingrui Liu ·

    How Edge of Stability Hinders SCAFFOLD in Federated Optimization

    arXiv:2608.25873v1 Announce Type: new Abstract: In federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing optimization algorithms that are unaffected by data heterogeneity, such as the SC…

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