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- W4312894843 abstract "Federated learning is an effective way to enable artificial intelligence over massive distributed nodes with security and communication efficiency. Some previous works primarily focus on learning a single global model for a unique task across the network, which is less competent to handle multi-task scenarios with stragglers and fault, after adopting the general gradient update methods in a federated environment. Others aim to learn a distinct model for each node, which is expensive in terms of the computation and communication cost. Using hierarchical network to reduce communication cost is becoming a new candidate. Thus, we propose a primal-and-dual method-based hierarchical federated multi-task learning system, supported with HFedMTL algorithm that allows massive nodes from distributed areas to join in the federated multi-task learning process. Empirical experiments verify the analysis and demonstrate the benefits of improving the learning performance and convergence rate." @default.
- W4312894843 created "2023-01-05" @default.
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- W4312894843 date "2022-09-12" @default.
- W4312894843 modified "2023-09-27" @default.
- W4312894843 title "HFedMTL: Hierarchical Federated Multi-Task Learning" @default.
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- W4312894843 doi "https://doi.org/10.1109/pimrc54779.2022.9977670" @default.
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