Scotch: An Efficient Secure Computation Framework for Secure Aggregation. (arXiv:2201.07730v1 [cs.CR])

Federated learning enables multiple data owners to jointly train a machine
learning model without revealing their private datasets. However, a malicious
aggregation server might use the model parameters to derive sensitive
information about the training dataset used. To address such leakage,
differential privacy and cryptographic techniques have been investigated in
prior work, but these often result in large communication overheads or impact
model performance. To mitigate this centralization of power, we propose
\textsc{Scotch}, a decentralized \textit{m-party} secure-computation framework
for federated …

framework