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Found 2 papers, 1 papers with code
Network Adaptive Federated Learning: Congestion and Lossy Compression
In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server.
Learning to Multi-Task by Active Sampling
In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision.
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