Energy and Loss-aware Selective Updating for SplitFed Learning with Energy Harvesting-Powered Devices

Chen, Xing; Li, Jingtao; Chakrabarti, Chaitali

doi:10.1007/s11265-022-01781-4

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SFL framework of [178] proposed an energy-and loss-aware selective updating method for heterogeneous systems, updating client-side models based on clients' energy and loss changes. Experiments were conducted using CIFAR [33] datasets.…”

Section: ) Research On Handling Heterogeneity Issues In Sflmentioning

confidence: 99%

Decentralized Learning in Healthcare: A Review of Emerging Techniques

2023

View full text Add to dashboard Cite

Recent developments in deep learning have contributed to numerous success stories in healthcare. The performance of a deep learning model generally improves with the size of the training data. However, there are privacy, ownership, and regulatory issues that prevent combining medical data into traditional centralized storage. Decentralized learning approaches enable collaborative model training by distributing the learning process among several nodes or devices. Conceptually, decentralized learning builds on earlier work in distributed optimization, but the focus of this paper is on recent and emerging techniques such as Federated Learning (FL), Split Learning (SL), and hybrid Split-Federated Learning (SFL). With common, universal deep learning models and centralized aggregator servers, FL overcomes the difficulties of centralized training. Additionally, patient data remains at the local party, upholding the security and anonymity of the data. SL enables machine learning without directly accessing data on clients or end devices. It further enhances privacy in a decentralized setting and mitigates clients' storage issues. In this survey, we first provide a contemporary survey of FL, SL, and SFL approaches. Second, we discuss their state-ofthe-art applications in healthcare, particularly in medical image analysis. Third, we review these emerging decentralized learning approaches under challenging conditions such as statistical and system heterogeneity, privacy preservation, communication efficiency, fairness, etc. Then, we address existing approaches to tackle these challenges. We detail unique complications related to healthcare applications including data, privacy and security, and communication challenges. Finally, we outline potential areas for further research on emerging decentralized learning techniques in healthcare, including developing personalized models, reducing bias, incorporating hybrid non-IID features, hyperparameter tuning, developing sufficient incentive mechanisms, and incorporating domain expertise knowledge.

show abstract

Section: ) Research On Handling Heterogeneity Issues In Sflmentioning

confidence: 99%

Decentralized Learning in Healthcare: A Review of Emerging Techniques

2023

View full text Add to dashboard Cite

show abstract

“…No averaging is performed on clientside models. Chen et al [16] studied another type of heterogeneity in terms of energy harvesting capability per client. Their proposed SplitFed algorithm was tested on CIFAR-10 and CIFAR-100 datasets.…”

Section: Related Workmentioning

confidence: 99%

Simon Fraser University's discourse processing lab, Burnaby, BC, Canada

Jahagirdar¹

2020

XRDS

View full text Add to dashboard Cite

SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learning, a model is trained by clients and a server collaboratively. For image segmentation, labels are created at each client independently and, therefore, are subject to clients' bias, inaccuracies, and inconsistencies. In this paper, we propose a data quality-based adaptive averaging strategy for SplitFed learning, called QA-SplitFed, to cope with the variation of annotated ground truth (GT) quality over multiple clients. The proposed method is compared against five stateof-the-art model averaging methods on the task of learning human embryo image segmentation. Our experiments show that all five baseline methods fail to maintain accuracy as the number of corrupted clients increases. QA-SplitFed, however, copes effectively with corruption as long as there is at least one uncorrupted client.

show abstract