Search Results for author: Mustafa Akdeniz

Found 2 papers, 0 papers with code

Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks

no code implementations12 Nov 2020 Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz, Yair Yona, Shilpa Talwar, Salman Avestimehr, Nageen Himayat

For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays.

Edge-computing Federated Learning

Coded Computing for Federated Learning at the Edge

no code implementations7 Jul 2020 Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz, A. Salman Avestimehr, Nageen Himayat

Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server.

Edge-computing Federated Learning +1

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