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Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
In this paper, we introduce a novel method called Fair, Robust, and Efficient Client Assessment (FRECA) for quantifying client contributions in FL.
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it.
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning
Then We propose three information-theoretic loss functions for deterministic GZSL model: a mutual information loss to bridge seen data and target classes; an uncertainty-aware entropy constraint loss to prevent overfitting when using seen data to learn the embedding of target classes; a semantic preserving cross entropy loss to preserve the semantic relation when mapping the semantic representations to the common space.
