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- W4308672058 abstract "Federated Learning is a machine learning paradigm where we aim to train machine learning models in a distributed fashion. Many clients/edge devices collaborate with each other to train a single model on the central. Clients do not share their own datasets with each other, decoupling computation and data on the same device. In this paper, we propose yet another adaptive federated optimization method and some other ideas in the field of federated learning. We also perform experiments using these methods and showcase the improvement in the overall performance of federated learning." @default.
- W4308672058 created "2022-11-14" @default.
- W4308672058 creator A5028362551 @default.
- W4308672058 date "2022-11-07" @default.
- W4308672058 modified "2023-09-27" @default.
- W4308672058 title "FedGrad: Optimisation in Decentralised Machine Learning" @default.
- W4308672058 doi "https://doi.org/10.48550/arxiv.2211.04254" @default.
- W4308672058 hasPublicationYear "2022" @default.
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