AIFL: Ensuring Unlinkable Anonymity and Robust Incentive in Cross-Device Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3417003
Abstract: While cross-device federated learning (FL) offers a privacy-preserving data processing approach for Internet of Things (IoT) devices, it introduces fresh privacy risks and elevated computational expenses. Current solutions prioritize data privacy, often overlooking identity privacy… read more here.
Keywords: cross device; robust incentive; privacy; unlinkable anonymity ... See more keywords
CSRA: Robust Incentive Mechanism Design for Differentially Private Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3329441
Abstract: The differentially private federated learning (DPFL) paradigm emerges to firmly preserve data privacy from two perspectives. First, decentralized clients merely exchange model updates rather than raw data with a parameter server (PS) over multiple communication… read more here.
Keywords: robust incentive; private federated; csra; dishonest clients ... See more keywords