BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks

• URL: http://arxiv.org/abs/2411.01050v1
• Date: Fri, 01 Nov 2024 21:34:43 GMT
• Title: BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
• Authors: Sushilkumar Yadav, Irem Bor-Yaliniz,
• Abstract summary: We propose the Bias-Aware Client Selection Algorithm (BACSA), which detects user bias and strategically selects clients based on their bias profiles. BACSA is suitable for sensitive healthcare applications where Quality of Service (QoS), privacy and security are paramount.
• Score: 0.5524804393257919
• License:
• Abstract: Federated Learning (FL) has emerged as a transformative approach in healthcare, enabling collaborative model training across decentralized data sources while preserving user privacy. However, performance of FL rapidly degrades in practical scenarios due to the inherent bias in non Independent and Identically distributed (non-IID) data among participating clients, which poses significant challenges to model accuracy and generalization. Therefore, we propose the Bias-Aware Client Selection Algorithm (BACSA), which detects user bias and strategically selects clients based on their bias profiles. In addition, the proposed algorithm considers privacy preservation, fairness and constraints of wireless network environments, making it suitable for sensitive healthcare applications where Quality of Service (QoS), privacy and security are paramount. Our approach begins with a novel method for detecting user bias by analyzing model parameters and correlating them with the distribution of class-specific data samples. We then formulate a mixed-integer non-linear client selection problem leveraging the detected bias, alongside wireless network constraints, to optimize FL performance. We demonstrate that BACSA improves convergence and accuracy, compared to existing benchmarks, through evaluations on various data distributions, including Dirichlet and class-constrained scenarios. Additionally, we explore the trade-offs between accuracy, fairness, and network constraints, indicating the adaptability and robustness of BACSA to address diverse healthcare applications.

Related papers

• Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare [9.433126190164224]
  We introduce a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL) We construct ambiguity sets around each client's empirical distribution that capture possible distribution shifts in the local data. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions.
  arXiv  Detail & Related papers  (2024-10-09T16:25:01Z)
• Submodular Maximization Approaches for Equitable Client Selection in Federated Learning [4.167345675621377]
  In a conventional Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. This paper introduces two novel methods, namely SUBTRUNC and UNIONFL, designed to address the limitations of random client selection.
  arXiv  Detail & Related papers  (2024-08-24T22:40:31Z)
• FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning [57.38427653043984]
  Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients. We introduce FedCAda, an innovative federated client adaptive algorithm designed to tackle this challenge. We demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance.
  arXiv  Detail & Related papers  (2024-05-20T06:12:33Z)
• One-Shot Federated Learning with Classifier-Guided Diffusion Models [44.604485649167216]
  One-shot federated learning (OSFL) has gained attention in recent years due to its low communication cost. In this paper, we explore the novel opportunities that diffusion models bring to OSFL and propose FedCADO. FedCADO generates data that complies with clients' distributions and subsequently training the aggregated model on the server.
  arXiv  Detail & Related papers  (2023-11-15T11:11:25Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation [95.85026305874824]
  We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
  arXiv  Detail & Related papers  (2022-12-14T13:57:01Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Personalized Federated Learning via Variational Bayesian Inference [6.671486716769351]
  Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. This paper proposes a novel personalized federated learning method via Bayesian variational inference named pFedBayes. Experiments show that the proposed method outperforms other advanced personalized methods on personalized models.
  arXiv  Detail & Related papers  (2022-06-16T07:37:02Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Auto-weighted Robust Federated Learning with Corrupted Data Sources [7.475348174281237]
  Federated learning provides a communication-efficient and privacy-preserving training process. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions. We propose Auto-weighted Robust Federated Learning (arfl) to provide robustness against corrupted data sources.
  arXiv  Detail & Related papers  (2021-01-14T21:54:55Z)

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.