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Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

arXiv 2108.13669 ↗First posted 31 Aug 2021eess.SP cs.LGcited by 0

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Abstract (arXiv)

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 31 Aug 2021.

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13 of 23 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  6. 2018Federated Learning via Over-the-Air Computationcited by 219
  7. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  8. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  9. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  10. 2020Massive Access for 5G and Beyondcited by 23
  11. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  12. 2020Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Taskscited by 4
1 more
  1. 2021Accelerating Federated Edge Learning via Optimized Probabilistic Device Schedulingcited by 2

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