pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data
- URL: http://arxiv.org/abs/2501.09822v1
- Date: Thu, 16 Jan 2025 20:16:49 GMT
- Title: pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data
- Authors: Zhou Ni, Masoud Ghazikor, Morteza Hashemi,
- Abstract summary: Traditional Federated Learning approaches often struggle with data heterogeneity across clients.
PFL emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) and unbalanced data across clients.
We formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach.
- Score: 1.9188272016043582
- License:
- Abstract: 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.
Related papers
- Over-the-Air Fair Federated Learning via Multi-Objective Optimization [52.295563400314094]
We propose an over-the-air fair federated learning algorithm (OTA-FFL) to train fair FL models.
Experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance.
arXiv Detail & Related papers (2025-01-06T21:16:51Z) - Adaptive Client Selection with Personalization for Communication Efficient Federated Learning [2.8484833657472644]
Federated Learning (FL) is a distributed approach to collaboratively training machine learning models.
This article introduces ACSP-FL, a solution to reduce the overall communication and computation costs for training a model in FL environments.
arXiv Detail & Related papers (2024-11-26T19:20:59Z) - Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning [10.917048408073846]
We introduce AFIND+, a simple yet efficient algorithm for sampling and aggregating neighbors in Decentralized FL (DFL)
AFIND+ identifies helpful neighbors, adaptively adjusts the number of selected neighbors, and strategically aggregates the sampled neighbors' models.
Numerical results on real-world datasets demonstrate that AFIND+ outperforms other sampling algorithms in DFL.
arXiv Detail & Related papers (2024-07-05T12:10:54Z) - Client Orchestration and Cost-Efficient Joint Optimization for
NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation.
We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
arXiv Detail & Related papers (2023-11-03T13:34:44Z) - Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets.
In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale.
We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
arXiv Detail & Related papers (2023-05-15T22:04:49Z) - Learning to Transmit with Provable Guarantees in Wireless Federated
Learning [40.11488246920875]
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks.
The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process.
Ultimately, our goal is to improve the accuracy and efficiency of the global FL model being trained.
arXiv Detail & Related papers (2023-04-18T22:28:03Z) - Semi-Synchronous Personalized Federated Learning over Mobile Edge
Networks [88.50555581186799]
We propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS$2$), over mobile edge networks.
We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds.
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss.
arXiv Detail & Related papers (2022-09-27T02:12:43Z) - Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
Federated learning (FL) offers a solution to train a global machine learning model.
FL suffers performance degradation when client data distribution is non-IID.
We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
arXiv Detail & Related papers (2021-08-12T14:18:05Z) - Low-Latency Federated Learning over Wireless Channels with Differential
Privacy [142.5983499872664]
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server.
In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
arXiv Detail & Related papers (2021-06-20T13:51:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.