Digital versus Analog Transmissions for Federated Learning over Wireless Networks
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Abstract (arXiv)
In this paper, we quantitatively compare these two effective communication schemes, i.e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios. We first examine both digital and analog transmission methods, together with a unified and fair comparison scheme under practical constraints. A universal convergence analysis under various imperfections is established for FL performance evaluation in wireless networks. These analytical results reveal that the fundamental difference between the two paradigms lies in whether communication and computation are jointly designed or not. The digital schemes decouple the communication design from specific FL tasks, making it difficult to support simultaneous uplink transmission of massive devices with limited bandwidth. In contrast, the analog communication allows over-the-air computation (AirComp), thus achieving efficient spectrum utilization. However, computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computational errors. Finally, numerical simulations are conducted to verify these theoretical observations.
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11 of 18 references.
References · 11
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
- 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
- 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
- 2019Federated Learning over Wireless Fading Channelscited by 178
- 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
- 2022Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingcited by 54
- 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
- 2020Federated Learning under Importance Samplingcited by 26
- 2023Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learningcited by 5
- 2022Intelligent Reflection Enabling Technologies for Integrated and Green Internet-of-Everything Beyond 5G: Communication, Sensing, and Securitycited by 2
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