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Found 5 papers, 2 papers with code
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches.
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more frequent occurrences, leading to poor performance on tasks with sparse rewards.
Federated Generalized Category Discovery
On each client, GCL builds class-level contrastive learning with both local and global GMMs.
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally.
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure.
