— 2025
Federated Learning
Gianluca Mittone
Abstract
This slide deck presents a 10-hour immersive course on Federated Learning (FL) fundamentals, delivered by Gianluca Mittone at the Master in High-Performance Computing (MHPC) at SISSA, May 13th–15th, 2026. The course is organized into 10 hours of theoretical lectures and 10 hours of practical exercises. Theoretical lectures cover six modules: (1) Introduction to Federated Learning; (2) Problem Formulation; (3) Practical Algorithmic Design; (4) Evaluation of Federated Learning Systems; (5) Limits of Standard Practices; and (6) Privacy, Robustness, Fairness, Personalization, followed by final remarks. Practical sessions guide students in building an FL simulator in a single Python process, re-implementing it in a distributed way using PyTorch's distributed backend, and experimenting with an off-the-shelf FL framework (Flower, optional). The course is based on "A Field Guide to Federated Optimization" by Wang et al. (2021). Key topics include the mathematical formulation of FL, FedAvg and its generalizations (FedOpt), communication efficiency, client sampling, data and computational heterogeneity, privacy-preserving protocols (SecAgg, differential privacy), robustness against poisoning attacks, fairness, personalization, and system constraints for real-world deployments. The course emphasizes the distinction between cross-device and cross-silo FL settings throughout, providing a comprehensive foundation for researchers and practitioners entering the field.
@misc{26:ictp:fl,
abstract = {This slide deck presents a 10-hour immersive course on Federated Learning (FL) fundamentals, delivered by Gianluca Mittone at the Master in High-Performance Computing (MHPC) at SISSA, May 13th--15th, 2026. The course is organized into 10 hours of theoretical lectures and 10 hours of practical exercises. Theoretical lectures cover six modules: (1) Introduction to Federated Learning; (2) Problem Formulation; (3) Practical Algorithmic Design; (4) Evaluation of Federated Learning Systems; (5) Limits of Standard Practices; and (6) Privacy, Robustness, Fairness, Personalization, followed by final remarks. Practical sessions guide students in building an FL simulator in a single Python process, re-implementing it in a distributed way using PyTorch's distributed backend, and experimenting with an off-the-shelf FL framework (Flower, optional). The course is based on "A Field Guide to Federated Optimization" by Wang et al. (2021). Key topics include the mathematical formulation of FL, FedAvg and its generalizations (FedOpt), communication efficiency, client sampling, data and computational heterogeneity, privacy-preserving protocols (SecAgg, differential privacy), robustness against poisoning attacks, fairness, personalization, and system constraints for real-world deployments. The course emphasizes the distinction between cross-device and cross-silo FL settings throughout, providing a comprehensive foundation for researchers and practitioners entering the field.},
address = {Trieste, Italy},
author = {Mittone, Gianluca},
howpublished = {Master in High-Performance Computing},
keywords = {icsc, dare, fl},
month = may,
title = {Federated Learning},
url = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m},
year = 2025,
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m}}