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Coded Matrix Computations for D2D-enabled Linearized Federated Learning

arXiv 2302.12305 ↗First posted 23 Feb 2023cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 23 Feb 2023.

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11 of 23 references.

References · 11

  1. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  2. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  3. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  4. 2017Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplicationcited by 32
  5. 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
  6. 2018On the Optimal Recovery Threshold of Coded Matrix Multiplicationcited by 27
  7. 2020Network-Aware Optimization of Distributed Learning for Fog Computingcited by 27
  8. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  9. 2020Coded Federated Learningcited by 14
  10. 2020Coded sparse matrix computation schemes that leverage partial stragglerscited by 5
  11. 2022Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchangecited by 5

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