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Federated Learning Algorithm FedALS Reduces Communication Costs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new federated learning algorithm called FedALS, designed to reduce communication costs while improving model generalization. The algorithm achieves this by employing differentiated aggregation frequencies for different parts of a model, specifically applying less frequent aggregations to the representation extractor (initial layers) and more frequent aggregations to the head (final layers). This approach is particularly effective in non-independent and identically distributed (non-iid) scenarios, as demonstrated by experimental results presented in the paper. AI

IMPACT This research could lead to more efficient and effective federated learning systems, particularly for decentralized datasets.

RANK_REASON The cluster contains a research paper detailing a new algorithm for federated learning. [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 Learning Algorithm FedALS Reduces Communication Costs

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31 / 100
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The cluster contains a research paper detailing a new algorithm for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Peyman Gholami, Hulya Seferoglu ·

    Differentiated Aggregation to Improve Generalization in Federated Learning

    arXiv:2404.11754v4 Announce Type: replace Abstract: This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning ba…

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