Abstract Personalized Federated Learning (PFL) aims to train machine learning models on
decentralized, heterogeneous data while preserving user privacy. This research survey …

Federated learning (FL) allows multiple clients cooperatively train models without disclosing
local data. However, the existing works fail to address all these practical concerns in FL …

Split learning (SL) has been recently proposed as a way to enable resource-constrained
devices to train multi-parameter neural networks (NNs) and participate in federated learning …

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-
preserving at the edge, in which densely distributed edge devices periodically exchange …

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 …

Federated learning (FL) is a promising distributed machine learning paradigm to train a
machine learning model without the leakage of local data. However, as the sizes of models …

Federated Learning (FL) provides a privacy-preserving framework for training machine
learning models on mobile edge devices. Traditional FL algorithms, eg, FedAvg, impose a …

Decentralized model training for on-road vehicles offers the potential to harness huge
amounts of data at low costs. However, existing approaches usually depend on the …

In this work, we study to release the potential of massive heterogeneous weak computing
power to collaboratively train large-scale models on dispersed datasets. In order to improve …

In multiple federated learning schemes, a random subset of clients sends in each round their
model updates to the server for aggregation. Although this client selection strategy aims to …