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4</head><body><div id="__next"><div><nav class="navbar navbar-expand-lg navbar-dark bg-dark"><div class="container"><a class="no-underline text-xl text-white mr-4" href="/">EIFCom</a><button aria-controls="responsive-navbar-nav" type="button" aria-label="Toggle navigation" class="navbar-toggler collapsed"><span class="navbar-toggler-icon"></span></button><div class="navbar-collapse collapse" id="responsive-navbar-nav"><div class="me-auto navbar-nav"><a class="no-underline px-2 py-2 text-gray-400 hover:text-gray-200" href="/">Home</a><a class="no-underline px-2 py-2 text-gray-400 hover:text-gray-200" href="/call_for_papers">Call for papers</a><a class="no-underline px-2 py-2 text-gray-400 hover:text-gray-200" href="/committees">Committees</a><a class="no-underline px-2 py-2 text-gray-400 hover:text-gray-200" href="/technical_program">Program</a><a class="no-underline px-2 py-2 text-gray-400 hover:text-gray-200" href="/paper_submission">Paper Submission</a></div></div></div></nav><div class="banner relative z-0" style="height:20vw"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img alt="banner" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="fill" class="object-cover" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%"/><noscript><img alt="banner" sizes="100vw" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=640&amp;q=75 640w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=750&amp;q=75 750w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=828&amp;q=75 828w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fwashinton-dc-mall.3aac9237.png&amp;w=3840&amp;q=75" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%" class="object-cover" loading="lazy"/></noscript></span><div class="leading-tight object-contain overflow-hidden backdrop-blur-sm relative top-1/3 text-white z-1 mx-auto text-center align-middle" style="height:10vw;width:60vw"><p class="mb-0" style="font-size:2vw">EIFCom 2024</p><p class="mb-0" style="font-size:1.5vw">International Workshop on Mobile Computing with Efficient and Interactive Foundation Models</p><p class="mb-0" style="font-size:1.2vw">In Conjunction with MobiCom 2024</p><p class="mb-0 " style="font-size:1.5vw">Nov. 18, 2024, Washington, D.C., USA</p></div></div></div><div class="container mx-auto"><p class="pt-2 justify-center text-center text-lg text-red-500 ">All times are in Eastern Time (ET).</p></div><div class="container mx-auto"><p class="pt-2 text-3xl font-serif">Schedule</p></div><div class="container grid grid-cols-12 px-0 "><div class="grid col-span-12 grid-cols-12 bg-[#bfdbfe]"><div class="col-span-2 text-center grid place-items-center"><span class="font-bold">Time</span></div><div class="col-span-10 pl-4"><span class="font-bold">Event</span></div></div><div class="grid col-span-12 grid-cols-12 bg-[#fde68a]"><div class="col-span-2 text-center grid place-items-center">14:00-14:05</div><div class="col-span-10 pl-4">Opening + Intro of keynote speaker</div></div>
4<div class="grid col-span-12 grid-cols-12 bg-[#e9d5ff]"><div class="col-span-2 text-center grid place-items-center">14:05-15:00</div><div class="col-span-10 pl-4">Opening Keynote</div></div><div class="grid col-span-12 grid-cols-12 bg-[#bbf7d0]"><div class="col-span-2 text-center grid place-items-center">15:00-15:40</div><div class="col-span-10 pl-4"><div><u>Paper Session 1</u></div><br/><div>TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation</div><div><i>Huanqi Yang (City University of Hong Kong), Rucheng Wu (City University of Hong Kong), Weitao Xu (City University of Hong Kong)</i><strong>--- Best Paper Award!</strong></div><br/><div>AutoJournaling: A Context-Aware Journaling System Leveraging MLLMs on Smartphone Screenshots</div><div><i>Tianyi Zhang (University of Melbourne), Shiquan Zhang (University of Melbourne), Le Fang (University of Melbourne), Hong Jia (University of Melbourne), Vassilis Kostakos (University of Melbourne), Simon D`Alfonso (University of Melbourne)</i><strong>--- Best Presentation Award!</strong></div></div></div><div class="grid col-span-12 grid-cols-12 bg-[#fecaca]"><div class="col-span-2 text-center grid place-items-center">15:40-15:55</div><div class="col-span-10 pl-4">Coffee Break</div></div><div class="grid col-span-12 grid-cols-12 bg-[#bbf7d0]"><div class="col-span-2 text-center grid place-items-center">15:55-16:35</div><div class="col-span-10 pl-4"><div><u>Paper Session 2</u></div><br/><div>Efficient and Personalized Mobile Health Event Prediction via Small Language Models</div><div><i>Xin Wang (University of Melbourne), Ting Dang (University of Melbourne), Vassilis Kostakos (University of Melbourne), Hong Jia (University of Melbourne)</i></div><br/><div>Stress-GPT: Stress detection with an EEG-based foundation model</div><div><i>Catherine Lloyd (Cardiff University), Loic Lorente Lemoine (Cardiff University), Reiyan Al-Shaikh (Cardiff University), Kim Tien Ly (University of Oxford), Hakan Kayan (Cardiff University), Charith Perera (Cardiff University), Nhat Pham (Cardiff University))</i></div></div></div><div class="grid col-span-12 grid-cols-12 bg-[#fde68a]"><div class="col-span-2 text-center grid place-items-center">16:35-16:40</div><div class="col-span-10 pl-4">Best Paper Award and Closing Remarks</div></div><div class="grid col-span-12 grid-cols-12 bg-[#fecaca]"><div class="col-span-2 text-center grid place-items-center">16:40-18:00</div><div class="col-span-10 pl-4">Networking</div></div></div><div class="container mx-auto"><p class="pt-2 text-3xl font-serif">Keynote Speakers</p></div><div class="container grid grid-cols-12 "><div class="col-span-12"><p class="text-2xl font-bold">Using Foundation Models to Coordinate Distributed Learning</p></div><div class="col-span-12 md:col-span-2"><div class="text-center"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span style="box-sizing:border-box;display:block;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;max-width:100%"><img style="display:block;max-width:100%;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0" alt="" aria-hidden="true" src="data:image/svg+xml,%3csvg%20xmlns=%27http://www.w3.org/2000/svg%27%20version=%271.1%27%20width=%272149%27%20height=%273224%27/%3e"/></span><img alt="Subhas" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="intrinsic" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%"/><noscript><img alt="Subhas" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcarlee.f614c836.png&amp;w=3840&amp;q=75 1x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcarlee.f614c836.png&amp;w=3840&amp;q=75" decoding="async" data-nimg="intrinsic" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%" loading="lazy"/></noscript></span></div><p class="text-xl font-serif text-center md:text-base lg:text-xl">Carlee Joe-Wong</p><div class="text-center"><a href="https://scholar.google.com/citations?user=XEztdZgAAAAJ&amp;hl=en" alt="google scholar"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span 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4<p class="pl-2 text-justify mb-2">Foundation models in machine learning are often used to capture broad patterns within large datasets, with the idea that they can then be fine-tuned or adapted to specific learning tasks. In distributed or federated settings, such foundation models are typically used to initialize a federated learning process: the model is distributed to all clients, which then proceed to alternate training the model on local data and synchronizing the resulting local models. In this talk, we explore using such a pre-trained foundation model not just as a model initialization but as a way to implicitly coordinate learning across distributed clients. We first consider a typical federated learning setting and show that incorporating the starting foundation model throughout the training can stabilize the training, alleviating the challenges of heterogeneous and non-i.i.d. (independent and identically distributed) data distributions across clients. We then consider a sensor network-inspired scenario where clients (i.e., sensors) collect correlated data. Since learning such correlations in real time would require significant sensor communication overhead and compromise sensors’ data privacy, we show that a pre-trained foundation model that captures these correlations can significantly improve sensor predictions.</p><p class="pl-2 text-justify"><span class="font-bold">Bio: </span>Carlee Joe-Wong is the Robert E. Doherty Associate Professor of Electrical and Computer Engineering at Carnegie Mellon University. She received her A.B., M.A., and Ph.D. degrees from Princeton University in 2011, 2013, and 2016 respectively. Her research interests lie in the optimization of networked systems, broadly speaking, most recently focusing on online and distributed machine learning in networked contexts. From 2013 to 2014, she was the Director of Advanced Research at DataMi, a startup she co-founded from her Ph.D. research on mobile data pricing. Her research has received several awards, including the NSF CAREER Award in 2018 and best paper and poster awards from IEEE INFOCOM, ACM SIGMETRICS, and ACM/IEEE IPSN.</p></div></div><div class="bg-blue-200 container h-8 mt-2"><p class="justify-center text-center text-xl font-semibold">Sponsor</p></div><div class="flex flex-row gap-40 justify-center mt-4"><div><a href="https://www.acm.org/" alt="ACM"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span style="box-sizing:border-box;display:block;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;max-width:100%"><img style="display:block;max-width:100%;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0" alt="" aria-hidden="true" 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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.