AAAI Spring Symposium: Federated Learning on Edge

Traditional Artificial Intelligence (AI) models predominantly rely on centralized computing architectures, limiting their potential in scenarios where real-time decision-making on low-latency devices is required. AI on The Edge has emerged to overcome these limitations, allowing AI algorithms and models to be deployed directly on edge devices, such as sensors, IoT devices, and autonomous systems. This shift in computation distribution reduces latency, improves responsiveness, and aims to enhance privacy, security, and bandwidth consumption. The next iteration for Edge AI is to allow devices to learn together and collaborate under a unified system architecture. The Federated Learning (FL) computational paradigm can facilitate this transition. This symposium invites academia, industry, and government researchers to explore Federated Learning on The Edge and its unique challenges and opportunities. The symposium will invite submissions of extended abstracts (to be developed into four-page manuscripts). In addition, the symposium will host invited keynote and session speakers. Overall, this symposium will offer a unique opportunity for participants from various backgrounds and agencies to engage in lively discussions, network with peers, and foster collaborations to advance and guide research and development for Federated Learning on The Edge.