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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
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  • Zhiyuan Wu ,
  • Sheng Sun ,
  • Yuwei Wang ,
  • Min Liu ,
  • Ke Xu ,
  • Wen Wang ,
  • Xuefeng Jiang ,
  • Bo Gao ,
  • Jinda Lu
Zhiyuan Wu
Institute of Computing Technology, Institute of Computing Technology, Institute of Computing Technology

Corresponding Author:[email protected]

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Sheng Sun
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Yuwei Wang
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Xuefeng Jiang
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Abstract

We propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample.
During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache.
Empirical experiments on four datasets demonstrate the comparable performance of FedCache with state-of-art PFL approaches, with more than two orders of magnitude of improvement in communication efficiency.
01 Feb 2024Submitted to TechRxiv
11 Feb 2024Published in TechRxiv