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cs.LG2026
Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
Ali Forootani, Raffaele Iervolino
This paper proposes a unified family of learnable Koopman operator parameterizations that integrate linear dynamical systems theory with modern deep learning forecasting architectu…
cs.LG2025
Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, traditional FL suffers from communication overhead…
cs.LG2024★ 1 cited
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data…