Abstract:
Cross-device federated learning (FL) enables the privacy-preserving and collaborative training of machine learning models across heterogeneous clients. To prevent gradien...Show MoreMetadata
Abstract:
Cross-device federated learning (FL) enables the privacy-preserving and collaborative training of machine learning models across heterogeneous clients. To prevent gradient information leakage, homomorphic encryption (HE) has been widely utilized due to its strong protection without losing accuracy. However, our experiments demonstrate that for clients with heterogeneous data and system capabilities, previous plain HE methods (i.e., encryption applied per gradient) and batch HE methods (i.e., encryption applied per batch of gradients) either significantly prolong training time or suffer from accuracy loss due to gradient quantization. We propose an adaptive batch HE framework for cross-device FL, which determines cost-efficient and sufficiently secure encryption strategies for clients with heterogeneous data and system capabilities. By leveraging the sparsity of convolutional neural networks for privacy-preserving similarity measurement of clients’ data, we first split the clients with similar data into their respective clusters. Then, we develop a fuzzy logic-based method to determine a cost-efficient and sufficiently secure HE key size for each client corresponding to its system capability, and arrange the clients with identical key size to the same groups. Finally, we design an efficient batch encryption approach for accuracy-lossless model aggregation. Extensive experiments on multiple heterogeneity scenarios demonstrate that our framework achieves comparable accuracy to plain HE, while reducing training time by 3\times – 31\times , and communication cost by 45\times – 66\times .
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 6, 15 March 2024)
Funding Agency:

School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
Junhao Han (Graduate Student Member, IEEE) received the B.E. degree in information security from Xi’an Jiaotong University, Xi’an, China, in 2021, where he is currently pursuing the M.S. degree with the School of Cyberspace Security.
His research interests include federated learning and intercloud computing.
Junhao Han (Graduate Student Member, IEEE) received the B.E. degree in information security from Xi’an Jiaotong University, Xi’an, China, in 2021, where he is currently pursuing the M.S. degree with the School of Cyberspace Security.
His research interests include federated learning and intercloud computing.View more

School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
Li Yan (Member, IEEE) received the B.S. degree in information engineering from Xi’an Jiaotong University, Xi’an, China, in 2010, and the Ph.D. degree in computer science from the University of Virginia, Charlottesville, VA, USA, in 2019.
He is currently an Associate Professor with the School of Cyber Science and Engineering, Xi’an Jiaotong University. He was a Postdoctoral Researcher with Senseable City Lab, Massachusetts ...Show More
Li Yan (Member, IEEE) received the B.S. degree in information engineering from Xi’an Jiaotong University, Xi’an, China, in 2010, and the Ph.D. degree in computer science from the University of Virginia, Charlottesville, VA, USA, in 2019.
He is currently an Associate Professor with the School of Cyber Science and Engineering, Xi’an Jiaotong University. He was a Postdoctoral Researcher with Senseable City Lab, Massachusetts ...View more

School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
Junhao Han (Graduate Student Member, IEEE) received the B.E. degree in information security from Xi’an Jiaotong University, Xi’an, China, in 2021, where he is currently pursuing the M.S. degree with the School of Cyberspace Security.
His research interests include federated learning and intercloud computing.
Junhao Han (Graduate Student Member, IEEE) received the B.E. degree in information security from Xi’an Jiaotong University, Xi’an, China, in 2021, where he is currently pursuing the M.S. degree with the School of Cyberspace Security.
His research interests include federated learning and intercloud computing.View more

School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
Li Yan (Member, IEEE) received the B.S. degree in information engineering from Xi’an Jiaotong University, Xi’an, China, in 2010, and the Ph.D. degree in computer science from the University of Virginia, Charlottesville, VA, USA, in 2019.
He is currently an Associate Professor with the School of Cyber Science and Engineering, Xi’an Jiaotong University. He was a Postdoctoral Researcher with Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA, from 2020 to 2021. His research interests include data-driven cyber–physical systems, big data analytics, and mobile computer networks.
Dr. Yan was the Best Transactions Paper Awardee of IEEE Transactions on Intelligent Transportation Systems and the Best-in-Session-Presentation Awardee of INFOCOM’2017.
Li Yan (Member, IEEE) received the B.S. degree in information engineering from Xi’an Jiaotong University, Xi’an, China, in 2010, and the Ph.D. degree in computer science from the University of Virginia, Charlottesville, VA, USA, in 2019.
He is currently an Associate Professor with the School of Cyber Science and Engineering, Xi’an Jiaotong University. He was a Postdoctoral Researcher with Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA, from 2020 to 2021. His research interests include data-driven cyber–physical systems, big data analytics, and mobile computer networks.
Dr. Yan was the Best Transactions Paper Awardee of IEEE Transactions on Intelligent Transportation Systems and the Best-in-Session-Presentation Awardee of INFOCOM’2017.View more