FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing

doi:10.1109/TPDS.2023.3289444

	FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing
	Wu, Zhiyuan 1,2; Sun, Sheng 1; Wang, Yuwei 1; Liu, Min 1,3; Pan, Quyang 1; Jiang, Xuefeng 1,2; Gao, Bo 4
	2024-06-01
发表期刊	IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
ISSN	1045-9219
卷号	35 期号:6 页码:952-966
摘要	The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Extensive experiments on three datasets demonstrate that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT in all considered scenarios.
关键词	Computational modeling Data models Training Servers Multitasking Adaptation models Optimization Distributed optimization federated learning knowledge distillation multi-access edge computing multi-task learning
DOI	10.1109/TPDS.2023.3289444
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001216308700001
出版者	IEEE COMPUTER SOC
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38997
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Yuwei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Zhongguancun Lab, Beijing 100190, Peoples R China 4.Beijing Jiaotong Univ, Engn Res Ctr Network Management Technol High Speed, Sch Comp & Informat Technol, Minist Educ, Beijing 100044, Peoples R China
推荐引用方式 GB/T 7714	Wu, Zhiyuan,Sun, Sheng,Wang, Yuwei,et al. FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2024,35(6):952-966.
APA	Wu, Zhiyuan.,Sun, Sheng.,Wang, Yuwei.,Liu, Min.,Pan, Quyang.,...&Gao, Bo.(2024).FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,35(6),952-966.
MLA	Wu, Zhiyuan,et al."FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 35.6(2024):952-966.