Privacy Analysis of Decentralized Federated Learning

Privacy concerns in federated learning have attracted considerable attention recently. In centralized networks, it has been observed that even without directly exchanging raw training data, the exchange of other so-called intermediate parameters such as weights/gradients can still potentially reveal private information. However, there has been...

Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Privacy issues and communication cost are both major concerns in distributed optimization in networks. There is often a trade-off between them because the encryption methods used for privacy-preservation often require expensive communication overhead. To address these issues, we, in this paper, propose a quantization-based approach to achieve...