Distributed federated learning is an emerging distributed computing paradigm where agents collaboratively learn machine-learning models via fully distributed (peer-to-peer) communication. Agents in the network do not need to share their own data, but exchange only model weights and/or gradients with neighbors, retaining privacy, and aggregate such received information with local models to improve accuracy. However, a potential issue of this setup is that malicious agents can covertly intrude the network and pollute the training, to reduce accuracy of models learned by the agents. In particular, if malicious agents are not promptly detected, they can irreversibly disrupt the collaborative learning process and ultimately degrade monitoring or decision-making tasks of normal agents.

You will be tasked to devise a resilient aggregation strategy that is robust to unknown adversaries, namely, it does not overly degrade accuracy of models learned by normal agents. A starting point, to be discussed with the supervisor, is the robust aggregation algorithm in [1] that integrates opinion dynamics to shield normal agents, which we will try to improve. Time permitting and depending on your interests, we might consider using trust observations to assess the legitimacy of transmissions up to a certain confidence [2,3] and/or specific applications such as medical imaging.

This thesis may involve a – possibly remote – collaboration with KTH, Sweden.

1. L. Ballotta, N. Bastianello, R: M. G. Ferrari, and K. H. Johansson, “Personalized and Resilient Distributed Learning Through Opinion Dynamics,” 2025 [under review at IEEE Transactions on Control of Network Systems]. Available at https://arxiv.org/abs/2505.14081

2. M. Yemini, A. Nedić, A. J. Goldsmith, and S. Gil, “Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems,” IEEE Transactions on Robotics, 2022. Available at https://arxiv.org/abs/2103.05464

3. L. Ballotta, Á. Vékássy, S. Gil, M. Yemini, “Confidence Boosts Trust-Based Resilience in Cooperative Multi-Robot Systems,” 2025 [under review at IEEE Transactions on Automatic Control]. Available at https://arxiv.org/abs/2506.08807