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Found 5 papers, 2 papers with code
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence
We theoretically show that the consensus mechanism can guarantee the convergence of the global objective.
UPFL: Unsupervised Personalized Federated Learning towards New Clients
To address this challenge, we extend the adaptive risk minimization technique into the unsupervised personalized federated learning setting and propose our method, FedTTA.
You Can Backdoor Personalized Federated Learning
The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts.
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE).
Practical and Light-weight Secure Aggregation for Federated Submodel Learning
Existing work fails to provide a loss-less scheme, or has impractical efficiency.
