TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

1 code implementation • 1 Nov 2022 • Vivek Khimani, Shahin Jabbari

With the increased legislation around data privacy, federated learning (FL) has emerged as a promising technique that allows the clients (end-user) to collaboratively train deep learning (DL) models without transferring and storing the data in a centralized, third-party server.

Federated Learning

SplitEasy: A Practical Approach for Training ML models on Mobile Devices

1 code implementation • 9 Nov 2020 • Kamalesh Palanisamy, Vivek Khimani, Moin Hussain Moti, Dimitris Chatzopoulos

In this work, we highlight the theoretical and technical challenges that need to be resolved to develop a functional framework that trains ML models in mobile devices without transferring raw data to a server.