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Found 5 papers, 5 papers with code
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data.
Rethinking Federated Learning With Domain Shift: A Prototype View
The private model presents degenerative performance on other domains (with domain shift).
Few-Shot Model Agnostic Federated Learning
To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared public datasets; 2) latent embedding adaptation, it addresses the domain gap with an adversarial learning scheme to discriminate the public and private domains.
Learn From Others and Be Yourself in Heterogeneous Federated Learning
Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data.
