On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
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Abstract (arXiv)
In this work, we consider a federated learning model in a wireless system with multiple base stations and inter-cell interference. We apply a differential private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system. In this setting, we assume that each user is equipped with a classifier. Moreover, the communication cells are assumed to have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 25 Aug 2022.
Builds on
26 of 51 references.
References · 26
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14 more
- 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
- 2017A Rewriting System for Convex Optimization Problemscited by 179
- 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
- 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
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- 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
- 2021Decentralized Wireless Federated Learning with Differential Privacycited by 18
- 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
- 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
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- 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
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