This repo contains code accompaning the papers listed below:

S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush, L. Barbieri "Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems," IEEE Communications Magazine, vol. 59, no. 2, pp. 16-21, February 2021 Online Available: https://arxiv.org/abs/2101.03367

S. Savazzi, M. Nicoli and V. Rampa, "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4641-4654, May 2020. Online Available: https://arxiv.org/pdf/1912.13163.pdf

Dataset for radars could be also downloaded from the IEEE Dataport repository: https://ieee-dataport.org/open-access/federated-learning-mmwave-mimo-radar-dataset-testing

Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, Mehdi Bennis "A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning," Online Available: https://arxiv.org/abs/2103.10346 (see also https://github.com/labRadioVision/federated_learning_carbon_footprint)

The code is written for Tensorflow 1.13.x and Tensorflow 2. In particular Tensorflow 1.x implementations are in the folder 'tensorflow1_implementations', Tensorflow 2.x compliant code is given in the folder 'tensorflow2_implementations', see also the readme files in both folders.