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Found 3 papers, 1 papers with code
Overcoming Noisy and Irrelevant Data in Federated Learning
A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training.
Model Pruning Enables Efficient Federated Learning on Edge Devices
To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model.
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as this requires collecting local measurements of each individual node and constantly sending those measurements to a central controller.
