I NDEX G UARD is introduced, an unsupervised index-only vetting primitive in which clients send only Top-K salient update indices and the server operates on the induced overlap geometry, clustering clients and filtering cohesion-outlier groups before aggregation.
FedMAST is proposed, a Federated Multi-Axis Structural Tracing defense for backdoor detection in federated learning that uses squeeze-pair coherence scoring to expose coupled feature distortions and signed spectral-drift tracking to reveal persistent directional changes over time.
Srinivasan Subramanian, Md Abdullah Al Hafiz Khan, Kazi Aminul Islam· 0 citations
Chorus is presented, a decentralized mechanism that lets each node detect and reject backdoored adapters from its neighbors before aggregation, without requiring shared validation data or any knowledge of the attacker's trigger or target.
Sayan Biswas, Jade Garcia Bourrée, R. Guerraoui et al.· 0 citations
This work introduces GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-para...
Baran Can Gül, Hanuma Siddhartha Tunuguntla, Anjana Arvind Naik et al.· 0 citations
This review examines four composable routes---parameter-efficient and quantized adaptation, backpropagation-free adaptation, proxy or submodel adaptation, and split federated adaptation---through a common framework that traces the objects each endpoint retains, exchanges, and discloses.
Yun-Heng Shen, Xiao Liu, Yang Yang et al.· Journal of Reliable and Secu...· 1 citation
Federated Learning (FL) enables collaborative model training across decentralized organizations without direct data sharing, yet communicating raw gradient updates remains susceptible to reconstruction and membership inference attacks. Fully Homomorphic Encryption (FHE) provides cryptographic privacy during aggregation...
Afonso Cruz, Carlos Marques, Vasco Carvalho et al.· Big Data and Cognitive Compu...· 0 citations
