3 papers
cs.LG2026
Quantization in Federated Learning: Methods, Challenges and Future Directions
Farwa Ikram, Dipanwita Thakur, Antonella Guzzo +1
Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication b…
cs.LG2024
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino +1
This paper proposes a novel federated algorithm that leverages momentum-based variance reduction with adaptive learning to address non-convex settings across heterogeneous data. We…
cs.LG2024
Anomalous Client Detection in Federated Learning
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespre…