7 papers
Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
Hao Chen, Zavareh Bozorgasl
Over-the-air federated learning (OTA-FL) reduces uplink latency by aggregating client updates directly over the wireless multiple-access channel. Coherent analog aggregation realiz…
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
Hao Chen, Chendi Qian, Christopher Morris +2
Exact solution of hard combinatorial optimization problems often relies on strong convex relaxations, but solving these relaxations repeatedly inside a branch-and-bound algorithm c…
SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation
Hao Chen, Zavareh Bozorgasl
We propose SCENE (Self-Centering Noncoherent Estimator), a pilot-free and phase-invariant aggregation primitive for over-the-air federated distillation (OTA-FD). Each device maps i…
An Innovative Networks in Federated Learning
Zavareh Bozorgasl, Hao Chen
This paper presents the development and application of Wavelet Kolmogorov-Arnold Networks (Wav-KAN) in federated learning. We implemented Wav-KAN \cite{wav-kan} in the clients. Ind…
Wav-KAN: Wavelet Kolmogorov-Arnold Networks
Zavareh Bozorgasl, Hao Chen
In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability…
Communication-Efficient Federated Learning via Clipped Uniform Quantization
Zavareh Bozorgasl, Hao Chen
This paper presents a novel approach to enhance communication efficiency in federated learning through clipped uniform quantization. By leveraging optimal clipping thresholds and c…