Federated Learning with Energy-Based Structured Probabilistic Inference
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their local data. However, heterogeneity in client data and the presence of unreliable or adversarial updates make effective server aggregation challenging, motivating methods that adapt client contributions according to their reliability and relationships. In this work, we formulate server aggregation in FL as structured probabilistic inference using conditional random fields. We represent clients as nodes in a graph and define unary and pairwise energy potentials. Unary potentials capture individual client properties, while pairwise potentials encode similarities between cli...
| Authors | Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 June 2026 |
| Last updated at source | 9 October 2026 |
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