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FedHeN: Federated Learning in Heterogeneous Networks

arXiv 2207.03031 ↗First posted 7 Jul 2022cs.LG cs.DCcited by 0

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Abstract (arXiv)

We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objective to the devices of higher complexities to jointly train different architectures in a federated setting. We empirically show that our approach improves the performance of different architectures and leads to high communication savings compared to the state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 7 Jul 2022.

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References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2018Group Normalizationcited by 1,514
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  9. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  11. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  12. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
6 more
  1. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  2. 2017Adaptive Neural Networks for Efficient Inferencecited by 161
  3. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  4. 2019Quantization Networkscited by 151
  5. 2015Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Treecited by 36
  6. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28

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