About the Workshop

Welcome to the 1st workshop on federated learning for Wireless Edge AI (FedEdgeAI). FedEdgeAI will be hosted in conjunction with the IEEE ICDCS 2025 conference, which will be held in Glasgow, Scotland, United Kingdom, from July 20th to July 23rd, 2025.

We invite you to submit your original work on topics related to federated learning, focusing on real-world challenges when federated learning is deployed in practical scenarios. This includes algorithms for distributed machine learning, adaptive techniques for changing network conditions, edge AI resilience, benchmarking generative models at the edge, downsizing Large Language Models (LLMs) into Small Language Models (SLMs) for improved computation and communication efficiency, semantic communication, asynchronous federated learning training, and rethinking communication protocols for wireless federated learning.

For more information about this event please click HERE.

Call for Papers

Edge AI emerged as an evolution of the edge computing paradigm, deploying AI algorithms and models directly on edge devices. Within this context, the concept of federated learning provides privacy by design in an machine learning technique, enabling collaborative learning across multiple distributed devices without sending raw data to a central server while processing data locally on devices. However, given the limited availability of resources on many devices, performing federated learning on such devices is impractical due to increased training times. Moreover, for training machine learning models that may be a Deep Neural Network (DNN), massive amounts of parameter updates need to be synchronized across distributed devices, creating potential congestion and eventually slowdowns the entire training process.

Specifically, the end devices used in federated learning are predominantly wireless and typically operate with limited bandwidth, such as 2G, 3G, or Wi-Fi. The global and local model parameters use uplink and downlink transmission, which depend on bandwidth resource block allocation, fading, and interference from others. Exchanging model parameters over such lossy networks may result in challenges such as transmission delay, which impacts the convergence time of the model, and packet losses, which affect the model’s accuracy.

We invite submissions on a wide range of topics including, but not limited to:

• Novel algorithms and architectures for the intersection of distributed machine learning and the cloud-edge-device continuum.
• Adaptive techniques for real-world constraints in federated learning deployments.
• Techniques for downsizing LLMs to SLMs for edge deployment (Edge Generative AI).
• Real-world applications of generative AI at the wireless edge.
• Benchmarking frameworks for evaluating generative AI models at the wireless edge.
• Semantic communications for wireless federated learning.
• Novel application layer and transport layer protocols at the wireless edge.
• Effective mobility management and migration mechanisms in wireless federated learning.
• Neural network optimization techniques in mobile scenarios.
• Privacy and security challenges in wireless federated learning.
• Communication-efficient techniques for asynchronous wireless federated learning training.
• Real testbeds and empirical evaluations.