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Federated Learning in Temporal Heterogeneity

arXiv 2309.09381 ↗First posted 17 Sep 2023cs.LGcited by 0

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Abstract (arXiv)

In this work, we explored federated learning in temporal heterogeneity across clients. We observed that global model obtained by textttFedAvg trained with fixed-length sequences shows faster convergence than varying-length sequences. We proposed methods to mitigate temporal heterogeneity for efficient federated learning based on the empirical observation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 17 Sep 2023.

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References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  8. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  9. 2015A Simple Way to Initialize Recurrent Networks of Rectified Linear Unitscited by 130
  10. 2016Recurrent Batch Normalizationcited by 35
  11. 2015Batch Normalized Recurrent Neural Networkscited by 14
  12. 2022Towards the Practical Utility of Federated Learning in the Medical Domaincited by 9

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