BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.3.6//EN TZID:Europe/London X-WR-TIMEZONE:Europe/London BEGIN:VEVENT UID:13@edgeaihub.co.uk DTSTART;TZID=Europe/London;VALUE=DATE:20250720 DTEND;TZID=Europe/London;VALUE=DATE:20250724 DTSTAMP:20250120T134511Z URL:https://edgeaihub.co.uk/events/workshop-on-federated-learning-for-wire less-edge-ai/ SUMMARY:Workshop on federated learning for Wireless Edge AI DESCRIPTION:\nAbout the Workshop\nWelcome to the 1st workshop on federated learning for Wireless Edge AI (FedEdgeAI). FedEdgeAI will be hosted in c onjunction with the IEEE ICDCS 2025 conference\, which will be held in  Glasgow\, Scotland\, United Kingdom\, from July 20th to July 23rd\, 202 5.\n\nWe invite you to submit your original work on topics related to fede rated learning\, focusing on real-world challenges when federated learning is deployed in practical scenarios. This includes algorithms for distribu ted machine learning\, adaptive techniques for changing network conditions \, edge AI resilience\, benchmarking generative models at the edge\, downs izing Large Language Models (LLMs) into Small Language Models (SLMs) for i mproved computation and communication efficiency\, semantic communication\ , asynchronous federated learning training\, and rethinking communication protocols for wireless federated learning.\n\nFor more information about t his event please click HERE.\n\n\nCall for Papers\nEdge AI emerged as an e volution of the edge computing paradigm\, deploying AI algorithms and mode ls directly on edge devices. Within this context\, the concept of federate d learning provides privacy by design in an machine learning technique\, e nabling collaborative learning across multiple distributed devices without sending raw data to a central server while processing data locally on dev ices. However\, given the limited availability of resources on many device s\, performing federated learning on such devices is impractical due to in creased training times. Moreover\, for training machine learning models th at may be a Deep Neural Network (DNN)\, massive amounts of parameter updat es need to be synchronized across distributed devices\, creating potential congestion and eventually slowdowns the entire training process.\n\nSpeci fically\, the end devices used in federated learning are predominantly wir eless and typically operate with limited bandwidth\, such as 2G\, 3G\, or Wi-Fi. The global and local model parameters use uplink and downlink trans mission\, which depend on bandwidth resource block allocation\, fading\, a nd interference from others. Exchanging model parameters over such lossy n etworks may result in challenges such as transmission delay\, which impact s the convergence time of the model\, and packet losses\, which affect the model’s accuracy.\n\nWe invite submissions on a wide range of topics in cluding\, but not limited to:\n\n Novel algorithms and architectures for the intersection of distributed machine learning and the cloud-edge-device continuum.\n Adaptive techniques for real-world constraints in federated learning deployments.\n Techniques for downsizing LLMs to SLMs for edge deployment (Edge Generative AI).\n Real-world applications of generative AI at the wireless edge.\n Benchmarking frameworks for evaluating generat ive AI models at the wireless edge.\n Semantic communications for wireles s federated learning.\n Novel application layer and transport layer proto cols at the wireless edge.\n Effective mobility management and migration mechanisms in wireless federated learning.\n Neural network optimization techniques in mobile scenarios.\n Privacy and security challenges in wire less federated learning.\n Communication-efficient techniques for asynchr onous wireless federated learning training.\n Real testbeds and empirical evaluations.\n\n\nImportant Dates\n\n Paper Submission Deadline: March 5 th\, 2025.\n Notification of Acceptance: April 2nd\, 2025\n Camera-Read y Submission: April 16th\, 2025\n Conference Dates: July 20th\, 2025\n\ n \;\n\n LOCATION:Glasgow\, Glasgow\, Glasgow\, United Kingdom X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Glasgow\, Glasgow\, United Kingdom;X-APPLE-RADIUS=100;X-TITLE=Glasgow:geo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:Europe/London X-LIC-LOCATION:Europe/London BEGIN:DAYLIGHT DTSTART:20250330T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST END:DAYLIGHT END:VTIMEZONE END:VCALENDAR