Bridging Federated Learning and Wireless Networks: From Adaptive Learning to FLdriven System Optimization

Federated learning (FL) has emerged as a promising distributed machine learning
framework that enables multiple devices to collaboratively train models without sharing raw
data, thereby preserving privacy and reducing the need for centralized data collection. However,
deploying FL in practical wireless environments introduces two major challenges. First, the data
generated across distributed devices are often heterogeneous and non-IID, which makes a single
global model insufficient for many users. Second, learning performance in wireless systems is
strongly affected by communication constraints such as interference, unreliable channels, and
dynamic resource availability. This PhD research aims to address these challenges by bridging
FL methods and wireless networks.
In the first thrust, we develop personalized and adaptive FL methods given the underlying
wireless link conditions. To this end, we propose channel-aware neighbor selection and
similarity-aware aggregation in wireless device-to-device (D2D) learning environments. We
further investigate the impacts of partial model update reception on FL performance. The
overarching goal of the first thrust is to enhance FL performance under wireless constraints.
Next, we investigate the opposite direction and raise the question: How can FL-based distributed
optimization be used for the design of next-generation wireless systems? To this end, we
investigate communication-aware participation optimization in vehicular networks, where
wireless resource allocation affects the number of clients that can successfully contribute to FL.
We further extend this direction to integrated sensing and communication (ISAC) systems,
where personalized FL (PFL) is used to support distributed beamforming optimization with joint
sensing and communication objectives.
Overall, this research establishes a unified framework for bridging FL and wireless networks. As
a future direction, this work will be extended to more realistic ISAC settings with dynamic
spectrum access, where communication, sensing, scheduling, and learning performance must be
considered jointly.