Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

Federated Learning (FL) fuses collaborative models from local nodes without centralizing
users' data. The permutation invariance property of neural networks and the non-iid data …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Existing meta-learning works assume that each task has available training and testing data.
However, there are many available pre-trained models without accessing their training data …

Federated learning presents massive potential for privacy-friendly vision task collaboration.
However, the federated visual performance is deeply affected by backdoor attacks, where …

We propose a method to fuse posterior distributions learned from heterogeneous datasets.
Our algorithm relies on a mean field assumption for both the fused model and the individual …