12 citations · 12 across the 5 of their papers we have counts for
5 papers
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization
Afsaneh Mahmoudi, Ming Xiao, Emil Björnson
Federated Learning (FL) enables clients to share learning parameters instead of local data, reducing communication overhead. Traditional wireless networks face latency challenges w…
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
Afsaneh Mahmoudi, Emil Björnson
Federated learning (FL) is a distributed learning framework where users train a global model by exchanging local model updates with a server instead of raw datasets, preserving dat…
Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks
Afsaneh Mahmoudi, Mahmoud Zaher, Emil Björnson
Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing comm…
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient
Afsaneh Mahmoudi, José Mairton Barros Da Silva Júnior, Hossein S. Ghadikolaei +1
Federated Learning (FL) plays a prominent role in solving machine learning problems with data distributed across clients. In FL, to reduce the communication overhead of data betwee…
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
Afsaneh Mahmoudi, Hossein S. Ghadikolaei, José Mairton Barros Da Silva Júnior +1
This paper investigates efficient distributed training of a Federated Learning~(FL) model over a wireless network of wireless devices. The communication iterations of the distribut…