FLEDGE24: AAAI Spring Symposium: Federated Learning on Edge Stanford University California, CA, United States, March 25-27, 2024 |
Conference website | https://sites.google.com/view/fledge2024 |
Abstract registration deadline | January 12, 2024 |
Submission deadline | January 12, 2024 |
Camera ready submission deadline | February 23, 2024 |
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.
Topics
- FL systems, topologies & architectures for the edge
- FL algorithmic optimizations for the edge
- FL for resource-constrained & unreliable edge devices
- FL for low size, weight, and power edge devices
- FL for 4G, 5G, 6G-and-beyond edge networks
- FL at the tactical edge
- FL for scalable, secure & private learning on the edge
- FL for lifelong learning on the edge
- FL for catastrophic forgetting on the edge
- Practical FL for the edge
- Hardware optimizations for FL on the edge
- Hardware-software co-design for FL on the edge
- Efficient Collaborative inference on the edge
- Open problems and challenges for FL on the edge
- Visionary perspectives for FL on the edge
Submission Guidelines
All keynote and invited speakers will provide a 1-page extended abstract to be included in the proceedings. For the Call for Papers (CFP), we will invite submissions of papers with a 4-page content length and unlimited pages for references and appendices. Every submitted paper will follow the AAAI 2024 format (https://aaai.org/authorkit24-2/) and will need to provide at least the “ML: Distributed Machine Learning & Federated Learning” keyword from the AAAI list. Other keywords can be found at (https://aaai.org/conference/aaai/aaai-23/keywords/). All authors can download the AAAI 2024 Word and LaTeX template from here as well: https://drive.google.com/file/d/186NfMBwL408frGzp6dSet_RdPyGkC2ea/view?usp=drive_link
Committees
Organizing committee
- Dimitris Stripelis, FedML Inc., University of Southern California
- George Sklivanitis, Center for Connected Autonomy and AI, Florida Atlantic University
- Joseph M Carmack, BAE Systems Inc.
- Jennifer M Sierchio, BAE Systems Inc.
- Rajeev Sahay, Department of Electrical and Computer Engineering, UC San Diego
Contact
Contact: fledge2024@gmail.com