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federatedlearning

Federated Learning: We are interested in studying the design of federated learning for distributed low capacity computing devices. These devices include mini-PCs, Single-Board-Computers and tiny computing devices (embedded systems).

Our approach is very practical. We use real devices in testbeds to experiment different designs of the federated learning protocol and their effects on resource consumption and accuracy.

The goal is to find more adaptive and decentralized federated learning designs.

Related publications in the group about this topic:
1. Marc Monfort Grau, Roger Pueyo Centelles, and Felix Freitag. On-Device Training of Machine Learning Models on Microcontrollers With a Look at Federated Learning. In Proceedings of the Conference on Information Technology for Social Good (GoodIT '21). Association for Computing Machinery, New York, NY, USA, 198–203. (postprint)
2. F. Freitag, P. Vilchez, Ch. Liu, L. Wei, M. Selimi. Testbed in Wireless City Mesh Network with Application to Federated Learning Experiments. ACM/SIGCHI International Conference on the Internet of Things (IoT 2021), Nov 2021, St.Gallen, Switzerland.
3. L. Ibraimi, M. Selimi, F. Freitag. BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices. IEEE Global Communications Conference (GLOBECOM 2021), Dec 2021, Madrid, Spain.
4. F. Freitag, P. Vilchez, Ch. Liu, L. Wei, M. Selimi. Performance Evaluation of Federated Learning over Wireless Mesh Networks with Low-Capacity Devices. International Conference on Information Technology & Systems (ICITS 2022), Feb 2022, San Carlos, Costa Rica.