Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates

no code implementations • 12 Aug 2021 • Srikanth Chandar, Pravin Chandran, Raghavendra Bhat, Avinash Chakravarthi

The communication of model weight updates in this distributed environment comes with significant network bandwidth costs.

Decoder Federated Learning

Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data

no code implementations • 28 Jun 2021 • Pravin Chandran, Raghavendra Bhat, Avinash Chakravarthi, Srikanth Chandar

Federated Learning allows training of data stored in distributed devices without the need for centralizing training data, thereby maintaining data privacy.

Federated Learning

NTP : A Neural Network Topology Profiler

no code implementations • 22 May 2019 • Raghavendra Bhat, Pravin Chandran, Juby Jose, Viswanath Dibbur, Prakash Sirra Ajith

In this paper, we present NTP (Neural Net Topology Profiler), a sophisticated benchmarking framework, to effectively identify memory and compute signature of an end-to-end topology on multiple hardware architectures, without the need for an actual implementation.

Benchmarking Quantization +2