FedGMR progressively restores sub-model capacity for bandwidth-constrained clients via gradual density increases and mask-aware aggregation, narrowing the gap to full-model federated learning.
Toward Understanding the Impact of Staleness in Distributed Machine Learning
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abstract
Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is challenging to directly monitor or control staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of stochastic gradient descent in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\sqrt{T}).
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HeLoCo corrects misaligned pseudo-gradients in asynchronous low-communication training via outer momentum reference, yielding up to 7.5% better loss at fixed tokens and 22.1% over synchronous under severe heterogeneity.
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HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity
HeLoCo corrects misaligned pseudo-gradients in asynchronous low-communication training via outer momentum reference, yielding up to 7.5% better loss at fixed tokens and 22.1% over synchronous under severe heterogeneity.