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CALM proposes a method for decentralized federated learning that uses class-wise agreement and label-gated disagreement modulation to handle non-IID data, improving teacher weighting and distillation trust without additional communication overhead.
D-FROST introduces a decentralized federated prompt-tuning algorithm using optimal transport to address challenges with non-IID and imbalanced data, ensuring convergence and effectiveness for foundation models.
FedImp is a novel federated learning algorithm that uses impurity-based weighting to enhance convergence speed in non-IID data settings, showing significant reductions in communication rounds compared to baselines.
This paper proposes FedLBW, a federated learning aggregation strategy that weights client updates by inverse validation loss instead of dataset size, improving accuracy and robustness to non-IID data and client dropouts in wireless networks.
Presents DG-FedReuse, a federated learning mechanism that reuses age-decayed cached client updates under a proxy-gradient threshold to reduce uplink communication, achieving significant modeled savings with minimal accuracy loss on image classification benchmarks.