2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Green Federated Learning: A new era of Green Aware AI
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino +1
The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly…