Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Despite federated learning FL's potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning CFL has emerged to address this challenge by partitioning users into clusters according to their...

Byzantine Outside, Curious Inside: Reconstructing Data through Malicious Updates

Federated learning FL enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal that privacy leakage is possible under commonly adopted FL protocols. In particular, a server with access to client...

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation

Federated Learning FL enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, wh...

GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis

The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. Steganalysis is profoundly hindered by the challenge of identifying subtle cognitive inconsistencies arising from textual fragmentation and complex dialogue structures, and th...

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks

In this paper, we investigate the problem of distributed learning DL in the presence of Byzantine attacks. For this problem, various robust bounded aggregation RBA rules have been proposed at the central server to mitigate the impact of Byzantine attacks. However, current DL methods apply RBA rul...