Federated learning (FL) has attracted increasing attention to building models without
accessing raw user data, especially in healthcare. In real applications, different federations …

There are two categories of methods in Federated Learning (FL) for joint training across
multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Federated learning (FL) is a machine learning paradigm where multiple client devices train
models collaboratively without data exchange. Data heterogeneity problem is naturally …

Recommender systems have become ubiquitous in the past years. They solve the tyranny of
choice problem faced by many users, and are utilized by many online businesses to drive …

Federated Learning (FL) and Split Learning (SL) are two popular paradigms of distributed
machine learning. By offloading the computation-intensive portions to the server, SL is …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

The federated learning privacy-preserving framework has achieved fruitful results in training
deep models across clients. This survey aims to provide a systematic overview of federated …

Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer
significant communication and computation costs. In contrast, one-shot and sequential …

In a real federated learning (FL) system, communication overhead for passing model
parameters between the clients and the parameter server (PS) is often a bottleneck …