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How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Yuchang Sun, Marios Kountouris, Jun Zhang
Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the mode…
Personalized Decentralized Federated Learning with Knowledge Distillation
Eunjeong Jeong, Marios Kountouris
Personalization in federated learning (FL) functions as a coordinator for clients with high variance in data or behavior. Ensuring the convergence of these clients' models relies o…
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +2
This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep…
Communication-Efficient Distributionally Robust Decentralized Learning
Matteo Zecchin, Marios Kountouris, David Gesbert
Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a ce…
Robust PAC: Training Ensemble Models Under Misspecification and Outliers
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +2
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers. PAC-Bayes theory demonstrates that the fr…
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
Apostolos Avranas, Marios Kountouris, Philippe Ciblat
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are alloc…