Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy

• URL: http://arxiv.org/abs/2507.07320v1
• Date: Wed, 09 Jul 2025 22:44:26 GMT
• Title: Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
• Authors: Dongyu Wei, Xiaoren Xu, Shiwen Mao, Mingzhe Chen,
• Abstract summary: We propose a secure and communication-efficient clustered federated learning (CFL) design.<n>In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training.<n>We propose a novel dynamic penalty function assisted value multi-agent reinforcement learning (DPVD-MARL) algorithm that enables distributed BSs to independently determine their connected users.
• Score: 28.120922916868683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a secure and communication-efficient clustered federated learning (CFL) design is proposed. In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training incorporating differential privacy (DP) techniques. Since each BS can process only a subset of the learning tasks and has limited wireless resource blocks (RBs) to allocate to users for federated learning (FL) model parameter transmission, it is necessary to jointly optimize RB allocation and user scheduling for CFL performance optimization. Meanwhile, our considered CFL method requires devices to use their limited data and FL model information to determine their task identities, which may introduce additional communication overhead. We formulate an optimization problem whose goal is to minimize the training loss of all learning tasks while considering device clustering, RB allocation, DP noise, and FL model transmission delay. To solve the problem, we propose a novel dynamic penalty function assisted value decomposed multi-agent reinforcement learning (DPVD-MARL) algorithm that enables distributed BSs to independently determine their connected users, RBs, and DP noise of the connected users but jointly minimize the training loss of all learning tasks across all BSs. Different from the existing MARL methods that assign a large penalty for invalid actions, we propose a novel penalty assignment scheme that assigns penalty depending on the number of devices that cannot meet communication constraints (e.g., delay), which can guide the MARL scheme to quickly find valid actions, thus improving the convergence speed. Simulation results show that the DPVD-MARL can improve the convergence rate by up to 20% and the ultimate accumulated rewards by 15% compared to independent Q-learning.

Related papers

• pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data [1.9188272016043582]
  Traditional Federated Learning approaches often struggle with data heterogeneity across clients.<n>PFL emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) and unbalanced data across clients.<n>We formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach.
  arXiv  Detail & Related papers  (2025-01-16T20:16:49Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks [15.195798715517315]
  Split federated learning (SFL) is a promising solution by of floading the primary training workload to a server via model partitioning.<n>We propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems.
  arXiv  Detail & Related papers  (2024-03-19T19:05:24Z)
• Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach [67.27031215756121]
  Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as emphConfederated Learning (CFL) in order to accommodate a larger number of users.
  arXiv  Detail & Related papers  (2024-02-28T03:27:10Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy [25.763777765222358]
  Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. As the model size grows, the training latency due to limited transmission bandwidth and private information degrades while using differential privacy (DP) protection. We propose sparsification empowered FL framework wireless channels, in over to improve training efficiency without sacrificing convergence performance.
  arXiv  Detail & Related papers  (2023-04-09T05:21:15Z)
• Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks [44.37047471448793]
  In this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL) We propose an innovative PSL framework, namely, efficient parallel split learning (EPSL) to accelerate model training. We show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy.
  arXiv  Detail & Related papers  (2023-03-26T16:09:48Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing [24.505675843652448]
  In this paper, we study a new latency optimization problem for federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously.
  arXiv  Detail & Related papers  (2022-03-18T00:38:29Z)
• Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data [42.26599494940002]
  Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model. This paper studies the potential of hierarchical FL in IoT heterogeneous systems. It proposes an optimized solution for user assignment and resource allocation on multiple edge nodes.
  arXiv  Detail & Related papers  (2021-07-14T08:32:39Z)
• 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.