To address these challenges, we introduce a collaborative decentralized learning paradigm called federated influencer learning (FIL), which aims to overcome the limitations of traditional FL. Terminologies The FIL framework consists of participants (local nodes) who are managed by an administrator ...
Federated learning presents a decentralized machine learning paradigm facilitating collaboration among multiple clients by harnessing local computational power and model transmission. Federated Learning (FL) encounters challenges, particularly concerning data leakage due to the lack of robust privacy-preserving me...
Federated Learning (FL) encounters challenges, particularly concerning data leakage due to the lack of robust privacy-preserving mechanisms during storage, transfer, and sharing processes. This poses significant risks to both data owners and suppliers. Existing FL systems often lack robust defense ...
As an active explorer and participant in federated learning, WeBank has led the way by releasing its industrial federated learning open-source framework, called FATE, to help improve the convenience and efficiency of building a federated learning solution and to quickly i...
homomorphic-encryption secure-computation federated-learning privacy-preserving-machine-learning zero-knowledge-proofs secure-multiparty-computation Updated Apr 26, 2022 C++ encryptogroup / ABY Star 469 Code Issues Pull requests ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation...
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realizationpp. 988-1001 by Yifeng Zheng, Shangqi Lai, Yi Liu, Xingliang Yuan, Xun Yi, Cong Wang VOLERE:基于个人语音挑战的抗泄漏用户认证协议 VOLERE: Leakage Resilient User Authentication Based on Personal Voice Cha...
Federated learning IoT IoMT Machine learning 1. Introduction In the past few years,exponential growthhas been observed in medical data generation through smart healthcare systems[1]. Most of the data are gathered using theInternet of Things(IoT). The IoT assimilated into various medical devices and...
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data
We propose a novel heterogeneous federated learning framework with model alignment, which assists participating institutions in aggregating heterogeneous models into a global large model via the parameter server, allowing medical institutions with varying resources to contribute to the federated learning proces...
Secure aggregation protocols ensure the privacy of users' data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant communication and computational burdens on participants and may not efficiently handle the large update vectors typical of machine ...