pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data
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Abstract (arXiv)
Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address this issue, Personalized Federated Learning (PFL) emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) and unbalanced data across clients. Furthermore, in most existing decentralized machine learning works, a perfect communication channel is considered for model parameter transmission between clients and servers. However, decentralized PFL over wireless links introduces new challenges, such as resource allocation and interference management. To overcome these challenges, we formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach. The proposed method, dubbed pFedWN, optimizes the learning performance for each client while accounting for the variability in D2D wireless channels. To tackle the formulated problem, we divide it into two sub-problems: PFL neighbor selection and PFL weight assignment. The PFL neighbor selection is addressed through channel-aware neighbor selection within unlicensed spectrum bands such as ISM bands. Next, to assign PFL weights, we utilize the Expectation-Maximization (EM) method to evaluate the similarity between clients' data and obtain optimal weight distribution among the chosen PFL neighbors. Empirical results show that pFedWN provides efficient and personalized learning performance with non-IID and unbalanced datasets. Furthermore, it outperforms the existing FL and PFL methods in terms of learning efficacy and robustness, particularly under dynamic and unpredictable wireless channel conditions.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 16 Jan 2025.
Builds on
29 of 56 references.
References · 29
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
- 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
- 2017Federated Multi-Task Learningcited by 669
- 2020Personalized Federated Learning with Moreau Envelopescited by 612
- 2020An Efficient Framework for Clustered Federated Learningcited by 505
- 2020Adaptive Personalized Federated Learningcited by 353
- 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
- 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
- 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
17 more
- 2019Federated Learning over Wireless Fading Channelscited by 178
- 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
- 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
- 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
- 2020Wireless Communications for Collaborative Federated Learningcited by 47
- 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
- 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
- 2023Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networkscited by 13
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
- 2022Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Realitycited by 9
- 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
- 2024Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selectioncited by 3
- 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
- 2023Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approachcited by 1
- 2024Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networkscited by 1
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.