Communication-Computation Efficient Secure Aggregation for Federated Learning

Understand

Federated learning has been spotlighted as a way to train neural networks using distributed data with no need for individual nodes to share data.

• Unfortunately, it has also been shown that adversaries may be able to extract local data contents off model parameters transmitted during federated learning.
• A recent solution based on the secure aggregation primitive enabled privacy-preserving federated learning, but at the expense of significant extra communication/computational resources.
• In this paper, we propose a low-complexity scheme that provides data privacy using substantially reduced communication/computational resources relative to the existing secure solution.