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Fair Resource Allocation in Federated Learning

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arxiv 1905.10497 v2 pith:KAZUW2ZU submitted 2019-05-25 cs.LG stat.ML

Tian Li , Maziar Sanjabi , Ahmad Beirami , Virginia Smith This is my paper · ORCID
classification cs.LGstat.ML
keywords federatednetworksq-fflfairlearningq-fedavgallocationdevices
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Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (specifically, a more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 356 citations worldwide. Full citation record

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    A unified convergence analysis of LoRA aggregation in federated learning shows Product-Sum aggregation converges globally at the optimal rate, while Sum-Product aggregation suffers from broadcast error from SVD truncation.

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    A critical review of 121 federated learning fairness papers identifies five recurring pitfalls and proposes a harm-centered, lifecycle-based framework.

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    A weighted-sum scalarization of agent and coordinator objectives yields a FedAvg-like decentralized algorithm with O(1/sqrt(T)) convergence under convexity and bounded heterogeneity.

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    cs.LG 2025-02 conditional novelty 4.0 of 10

    A federated-learning plus graph-neural-network caching method is tested on MovieLens and achieves cache hit rates slightly above an autoencoder-based federated benchmark.

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