FLUID workshop @ AAAI 2025

The FLUID workshop focuses on tackling key challenges in Federated Learning (FL), such as adaptability and scalability in decentralized AI systems, especially in environments with non-IID data and varied device capabilities. It aims to bridge the gap between theoretical advances and real-world applications by highlighting practical methodologies and case studies. While adaptability, decentralization, and heterogeneity in FL have been extensively explored in academia, a significant gap remains between research and real-world application. Many solutions are limited to simulations, with few scalable systems deployed in industries like healthcare, smart cities, finance, and autonomous systems. FLUID aims to bridge this gap by focusing on practical, actionable solutions that are ready for real-life implementation. The workshop promotes collaboration among researchers and industry experts to drive innovation in healthcare, autonomous systems, and smart cities. The goal is to advance the deployment of resilient, scalable, and intelligent decentralized systems through FL. Topics of interest include, but are not limited to: - Novel algorithms for handling statistical and device heterogeneity in FL - Scalability and robustness in decentralized FL systems - Adaptive learning frameworks for dynamic and non-stationary environments - Practical applications of FL in healthcare, autonomous systems, finance, smart cities, and IoT networks - Fairness and bias mitigation in FL across heterogeneous data sources - Benchmarking and evaluation methods for real-world FL deployments - Communication-efficient FL algorithms for resource-constrained environments - Real-world case studies and success stories of FL deployment - Tools and platforms for deploying FL systems at scale - Integration of FL with edge computing and multi-agent systems Mail to: fluid.workshop.2024@gmail.com