6G networks envision ubiquitous computing and connectivity which will ultimately lead to massive growth in data traffic and billions of edge nodes connected with each other. To avoid delays and single point of failure in huge networks, edge devices are now widely employed for various applications, such as intelligent transportation systems, surveillance, and home automation. However, in many scenarios, sophisticated Artificial Intelligence (AI) algorithms are required consuming significant amount of processing power and occupying large storage size which may exceed the available resources of typical edge devices. To overcome this challenge, recent delay sensitive, distributed, and intelligent trends in computing paradigms such as Tiny Machine Learning, Federated Learning, Mobile Edge Computing, Multiaccess Edge Computing, Fog Computing and Computational Offloading are under research, aiming to optimize latency, computing complexity, and resourceful utilization of bandwidth, thus giving rise to a potential research direction of distributed and Intelligent Edge Computing (IEC). Due to significant tasks expected to be handled by edge devices in 6G communications, IEC is deemed to play an important role. To support distributed AI applications on the edge computing platform, efficient life-cycle management and closed-loop automation tools are required to manage the highly heterogeneous computing elements in edge computing (e.g., embedded devices, intelligent base stations, edge and fog, servers, etc.). Also, novel methods are needed to ensure IEC security against attacks, the privacy of the data their models and their trustworthiness, avoiding erroneous decisions and ensuring high performance AI/ML models.
This workshop aims invites researchers from industry and academia to share their recent findings and views on technical advances in IEC and distributed communications. Please see Special Session Description for the details.