Call for Papers

FedEdge 2024 welcomes submissions from both researchers and practitioners from academia and industry that explore the latest developments in data privacy and federated learning technologies on edge networks. In addition, we encourage work-in-progress and position papers that describe highly original ideas, present new directions, or have the potential to generate insightful provocative discussion at the workshop.

Motivated by emerging end-devices and edge networks, applications such as VR, autonomous driving, and IoT are emerging quickly. As data privacy has attracted more and more attention, how to protect the data generated from end-devices is becoming a common concern. In order to achieve intelligent edge networks on the premise of ensuring data privacy, advances need to be made in distributed computing, federated learning, edge blockchain, data encryption, and model compression, all occurring at the edge and under constraints this imposes. Moreover, such designs need to occur in concert with operations among multiple edges. Improving data security and making use of such huge amounts of edge data can be significantly aided by the development of FL and other new methodologies.

Submission Instructions

Submissions must be original, unpublished work, and not currently under consideration elsewhere. Submitted papers must be no longer than five (5) pages, including all figures, tables, followed by 1 page references. Submission should be a single PDF file with all fonts embedded, in two-column 10pt ACM format with authors names and affiliations for single-blind peer review. Prospective authors are encouraged to use the same PDF formatting guidelines as the main conference. Authors of accepted papers are expected to present their work at the workshop. Accepted papers will be published in the MobiCom Workshop Proceedings, and available at the ACM Digital Library.

Topic of Interests

The goal of this FedEdge Workshop is to bring together scientists, researchers, and engineers to identify new problems, latest novel topics, and emerging technologies. We focus on all aspects of edge network, data privacy and federated technologies, including but not limited to the following:

• Security, fairness and privacy in edge caching
• Reinforcement learning and MAB algorithms for resource management at the edge
• Federated machine learning among multiple edge networks
• Multi-task learning methods for various edge applications
• Knowledge distillation and model miniaturization on edge servers
• Game-theoretic and multi-agent approaches to distributed algorithms at the edge
• Modeling, monitoring and analysis of heterogeneous edge networks

Submission : FedEdge2024 HotCRP

Important Dates

• Workshop Paper Submissions: July 22, 2024 (Extended!)
• Notification of acceptance: September 2, 2024
• Camera-ready Workshop Papers: September 09, 2024
• Workshop Dates: November 18, 2024