Federated learning (FL) has emerged as a promising framework for distributed machine
learning. It enables collaborative learning among multiple clients, utilizing distributed data …

Differentially private distributed mean estimation (DP-DME) is a fundamental building block
in privacy-preserving federated learning, where a central server estimates the mean of $ d …

Recommender systems often rely on graph-based filters, such as normalized item-item
adjacency matrices and low-pass filters. While effective, the centralized computation of these …

Decentralized learning enables distributed agents to train a shared machine learning model
through local computation and peer-to-peer communication. Although each agent retains its …

The collapse of storage and data processing costs, along with the rise of digitization, has
brought new applications and possibilities to machine learning. In practice, Big data is often …