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Found 3 papers, 1 papers with code
PeFLL: Personalized Federated Learning by Learning to Learn
We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones.
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning
We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning.
Charge capacity characteristics of a Lithium Nickel-Cobalt-Aluminium Oxide battery show fractional-derivative behavior
Batteries experience capacity offset where available charge depends on the rate at which this charge is drawn.
