Postdoc
Angelo Rodio is a Postdoctoral Researcher at Linköping University, Sweden.
He works on decentralized machine learning with Professor Erik G. Larsson and Associate Professor Zheng Chen.
He received his PhD from Inria, France, in July 2024, working on federated learning.
His publications are available on Google Scholar, and more details can be found on his personal webpage.
Angelo Rodio, Giovanni Neglia, Zheng Chen, Erik G Larsson, 
A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication, FORTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-26, VOL 40 NO 30, AAAI Conference on Artificial Intelligence, pp. 25151-25159, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE (2026) 
https://doi.org/10.1609/aaai.v40i30.39705
Chuan Xu, Caelin Kaplan, Angelo Rodio, Tareq Si Salem, Giovanni Neglia, Federated Learning for Collaborative Inference Systems: The case of early exit networks, Performance evaluation (Print) 171:102538 (2026) https://doi.org/10.1016/j.peva.2025.102538
Angelo Rodio, Giovanni Neglia, Zheng Chen, Erik G. Larsson, A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication (2025) 
https://arxiv.org/abs/2511.11560
Angelo Rodio, Zheng Chen, Erik G. Larsson, Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise (2025) https://arxiv.org/abs/2501.14644
Angelo Rodio, Giovanni Neglia, FedStale: leveraging stale client updates in federated learning (2024) https://arxiv.org/abs/2405.04171