posted by organizer: gsklivanitis || 973 views || tracked by 1 users: [display]

FLEDGE 2024 : Federated Learning on the Edge


When Mar 25, 2024 - Mar 27, 2024
Where Stanford, CA
Submission Deadline Jan 12, 2024
Final Version Due Feb 23, 2024
Categories    federated learning   edge ai   distributed learning   distributed networks

Call For Papers

Computational intelligence bears the prospect of a trendsetting technology able to unlock solutions to previously difficult and large-scale problems outside of the current cloud-centric paradigm. In the following decades, intelligent agents trained in the cloud using machine learning algorithms on large amounts of data will be deployed in the real world. Under the requirements of dynamic applications, AI agents sharing a common goal will be designed on the fly. Therefore, real-time interactions between AI agents will be necessary to solve complex distributed problems where massive connectivity, large data volumes, and ultra-low latency are beyond those offered by 5G networks and beyond. To harness the true power of such agents, Federated Learning on the Edge is the key.

Federated Learning (FL) has recently emerged as a standard distributed machine learning computational paradigm to meet these needs by enabling coordination and cooperation among such agents on the Edge. FL was initially proposed for text recommendation on mobile phones to improve the communication efficiency of devices, i.e., by not sending their data to a central repository. However, FL has witnessed vast applicability across many disciplines, especially in healthcare, finance, and manufacturing. Since FL allows data to remain at the source, sources only need to share their locally trained model parameters. By preserving data locality, FL can reduce the data security and privacy risks associated with aggregating data in a single location.

Through this symposium, we want to create a collaborative platform to address open issues frequently observed in FL on the Edge. Edge devices in a FL environment may experience computational power, memory capacity, and/or communication bandwidth limitations. Participating devices may have heterogeneous hardware equipment or be powered by small-capacity batteries, leading to network disconnections and packet drops. These challenges require novel algorithmic approaches and system solutions that can facilitate the deployment of FL in such resource-constrained computational environments. Considering the resource-intensive requirements of developing different security and privacy protocols on edge, providing solutions from a theoretical and practical point of view makes these challenges particularly attractive.

We invite advances combining FL with on-device intelligence. Our primary focus is FL systems and algorithms for AI on edge devices and hardware and communication optimizations for enabling AI on the edge using FL. Theoretical, empirical, and application-focused works are also welcome. The topics of interest include, but are not limited to, the following:

-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
-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

Related Resources

ICMLA 2024   23rd International Conference on Machine Learning and Applications
FLTA 2024   The 2nd IEEE International Conference on Federated Learning Technologies and Applications
MLNLP 2024   2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024)
AMLDS 2025   2025 International Conference on Advanced Machine Learning and Data Science
DSIT 2024   2024 7th International Conference on Data Science and Information Technology (DSIT 2024)
ITNG 2024   The 21st Int'l Conf. on Information Technology: New Generations ITNG 2024
MLIS 2024   The 6th International Conference on Machine Learning and Intelligent Systems (MLIS 2024)
AASDS 2024   Special Issue on Applications and Analysis of Statistics and Data Science
SI AIMLDE 2024   SPECIAL ISSUE on Applied Artificial intelligence, Machine Learning, and Data Engineering
IITUPC 2024   Immunotherapy and Information Technology: Unleashing the Power of Convergence