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

no code implementations • 12 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 implementations • 7 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

Coded Federated Learning

no code implementations • 21 Feb 2020 • Sagar Dhakal, Saurav Prakash, Yair Yona, Shilpa Talwar, Nageen Himayat

Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data.

Federated Learning