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Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding

arXiv 2112.03267 ↗First posted 5 Dec 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

Mobile devices are indispensable sources of big data. Federated learning (FL) has a great potential in exploiting these private data by exchanging locally trained models instead of their raw data. However, mobile devices are often energy limited and wirelessly connected, and FL cannot cope flexibly with their heterogeneous and time-varying energy capacity and communication throughput, limiting the adoption. Motivated by these issues, we propose a novel energy and communication efficient FL framework, coined SlimFL. To resolve the heterogeneous energy capacity problem, each device in SlimFL runs a width-adjustable slimmable neural network (SNN). To address the heterogeneous communication throughput problem, each full-width (1.0x) SNN model and its half-width (0.5x) model are superposition-coded before transmission, and successively decoded after reception as the 0.5x or 1.0x model depending on the channel quality. Simulation results show that SlimFL can simultaneously train both 0.5x and 1.0x models with reasonable accuracy and convergence speed, compared to its vanilla FL counterpart separately training the two models using 2x more communication resources. Surprisingly, SlimFL achieves even higher accuracy with lower energy footprints than vanilla FL for poor channels and non-IID data distributions, under which vanilla FL converges slowly.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 5 Dec 2021.

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14 of 26 references.

References · 14

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  7. 2018Slimmable Neural Networkscited by 305
  8. 2019Universally Slimmable Networks and Improved Training Techniquescited by 221
  9. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  10. 2020Federated Knowledge Distillationcited by 51
  11. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  12. 2020Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theorycited by 16
2 more
  1. 2021Communication-Efficient and Personalized Federated Lottery Ticket Learningcited by 10
  2. 2018URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systemscited by 2

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  1. 2022SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
  2. 2021Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
  3. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
  4. 2024Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique
  5. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  6. 2023Communication-Efficient Federated Learning via Regularized Sparse Random Networks
  7. 2022CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
  8. 2025Lightweight Federated Learning over Wireless Edge Networks