no code implementations • 22 Apr 2024 • Mohak Chadha, Alexander Jensen, Jianfeng Gu, Osama Abboud, Michael Gerndt
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized.
1 code implementation • 11 Feb 2024 • Mohak Chadha, Pulkit Khera, Jianfeng Gu, Osama Abboud, Michael Gerndt
To address these challenges and enable heterogeneous client models in serverless FL, we utilize Knowledge Distillation (KD) in this paper.
1 code implementation • 7 Feb 2024 • Jannis Weil, Zhenghua Bao, Osama Abboud, Tobias Meuser
The size of the observed neighborhood limits the generalizability to different graphs and affects the reactivity of agents, the quality of the selected actions, and the communication overhead.
1 code implementation • 10 Nov 2022 • Mohamed Elzohairy, Mohak Chadha, Anshul Jindal, Andreas Grafberger, Jianfeng Gu, Michael Gerndt, Osama Abboud
We implement our strategy by extending an open-source serverless FL system called FedLess.