Split learning reduces client-side computation and limits the direct exposure of private data, but it does not fully eliminate privacy leakage because the server can still observe intermediate smashed representations. These representations may preserve sensitive attributes of client inputs and enable property inference...
Nexhi Sula, Nafiza Tabassoum, Jie Hou et al.· IEEE Conference on Communica...· 0 citations
The Federated Learning (FL) paradigm, while promising for decentralized training, remains susceptible to privacy breaches via gradient leakage. While numerous defenses have been established for synchronous systems, these mechanisms often rely on batched aggregation to mask individual contributions, a property absent in...
Shamim Yazdani, Noah Guzinski, George Torres et al.· IEEE Conference on Communica...· 0 citations
Internal representations thus support cross-mechanism attack-exposure auditing against calibrated benign references, but decouple from downstream task compromise and parameter integrity, according to downstream task compromise and parameter integrity.
Zhen Guo, Shang-Hao Shi, Shamim Yazdani et al.· 0 citations
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