CS-E4740 Clustered FL скачать в хорошем качестве

CS-E4740 Clustered FL

🔗 Clustered Federated Learning Demystified | CS-E4740 Lecture with Prof. Alexander Jung How can we train different models for different groups of users—all without ever sharing raw data? Welcome to Clustered Federated Learning (CFL). In this lecture from the Aalto University course CS-E4740 - Federated Learning, Assoc. Prof. Alexander Jung introduces CFL as a powerful extension of standard FL. Instead of forcing a one-size-fits-all global model, CFL discovers natural clusters of clients—each with their own tailored model. 📌 In this session, you'll learn: How GTVMin (Generalized Total Variation Minimization) naturally leads to model clustering Why edge weights and FL network design are critical for correct cluster recovery How to construct FL networks from data using statistical tools like statistical tests, KL divergence estimates, and embedding comparisons 🎯 By the end, you’ll understand how CFL offers a middle ground between global and personalized FL—balancing scalability with flexibility. 👉 Resources mentioned: 📘 Aalto Dictionary of Machine Learning: https://aaltodictionaryofml.github.io... 🎥 Full course playlist:    • CS-E4740 Federated Learning ('25)   🌐 Course site: https://federatedlearningaalto.github... 🔔 Subscribe for more machine learning insights:    / @alexjung111   #ClusteredFederatedLearning #federatedlearning #machinelearning #variation #privacypreservingml #AaltoUniversity #AlexJung #federatedlearning #distributedcomputing ‪@ieeeSPS‬ #artificialintelligence #fairness #democracy