<div dir="ltr"><div dir="ltr"><div><div class="gmail_default" style="font-family:georgia,serif;font-size:small;color:rgb(0,0,0)"><b>When: </b>Wednesday, June 11th at <b style="background-color:rgb(255,255,0)">11am CT</b></div><div><div><font face="georgia, serif" color="#000000"><br></font></div><div><font face="georgia, serif" color="#000000"><b>Where:</b> <b> </b><span style="background-color:rgb(255,255,0)">Talk will be given <font style="font-weight:bold"><u>live, in-person</u></font><font style="font-weight:bold"> </font>at</span></font></div><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font color="#000000"><font face="georgia, serif"> </font><font face="georgia, serif" style="background-color:rgb(255,255,0)"> </font><font face="georgia, serif" style="background-color:rgb(255,255,0)">TTIC, 6045 S. Kenwood Avenue</font></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="georgia, serif" color="#000000"> <span style="background-color:rgb(255,255,0)">5th Floor, <span class="gmail_default"></span></span><span style="background-color:rgb(255,255,0)"><b>Room 5<span class="gmail_default">29</span></b></span><b style="background-color:rgb(255,255,0)"> </b><b> </b> </font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="georgia, serif" color="#000000"><br></font></p><p class="MsoNormal" style="margin:0in;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="georgia, serif" color="#000000"><span class="gmail_default"><b>Virtually: </b>via <a href="https://uchicago.zoom.us/j/96211938253?pwd=T6huTrQkHiJCnIwFcmzUuik9OlvNSZ.1" target="_blank">Zoom</a></span><br></font></p></div><div class="gmail_default"><font face="georgia, serif" color="#000000"><b><br></b></font></div><div class="gmail_default"><font face="georgia, serif" color="#000000"><b>Who</b>: Kumar Kshitij Patel, TTIC</font></div><div class="gmail_default"><b><font face="georgia, serif" color="#000000"><br></font></b></div><div class="gmail_default"><font face="georgia, serif" color="#000000"><b>Title: </b>What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness</font></div><div class="gmail_default"><font face="georgia, serif" color="#000000"><br></font></div><div><font face="georgia, serif" color="#000000"><b>Abstract:</b> Over the past decade, Federated Learning (FL) has emerged as a powerful framework that enables learning from multiple decentralized datasets without the need to exchange raw data. This paradigm has led to significant advancements across various fields, including healthcare, finance, research, and consumer technologies. However, despite extensive research in FL, important foundational questions remain unanswered. One such question pertains to the effectiveness of local update algorithms, particularly in understanding when these algorithms outperform traditional distributed optimization methods.</font></div><font face="georgia, serif" color="#000000"><br>This thesis addresses this question by focusing on Local Stochastic Gradient Descent (Local SGD), also known as Federated Averaging, which is the simplest and most commonly used local update algorithm in FL. We present non-asymptotic convergence analyses for Local SGD in convex settings, deriving tight upper and lower bounds that reveal how second-order data heterogeneity and third-order smoothness dictate its communication efficiency.<br><br>Additionally, we enhance Local SGD by incorporating variance reduction techniques, resulting in an optimal algorithm specifically designed for distributed non-convex optimization. Throughout this exploration, the thesis also characterizes minimax complexity for several significant distributed optimization problems. Finally, we examine how distribution shifts and adaptive adversaries affect distributed online and bandit optimization frameworks, proposing new algorithms and offering insightful analyses. </font><br clear="all"></div><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><b style="background-color:rgb(255,255,255)"><font color="#3d85c6">Brandie Jones </font></b><div><div><div><font color="#3d85c6"><b><i>Executive </i></b></font><b style="color:rgb(61,133,198)"><i>Administrative Assistant</i></b></div></div><div><span style="background-color:rgb(255,255,255)"><font color="#3d85c6">Toyota Technological Institute</font></span></div><div><span style="background-color:rgb(255,255,255)"><font color="#3d85c6">6045 S. Kenwood Avenue</font></span></div><div><span style="background-color:rgb(255,255,255)"><font color="#3d85c6">Chicago, IL 60637</font></span></div></div><div><span style="background-color:rgb(255,255,255)"><font color="#3d85c6"><a href="http://www.ttic.edu" target="_blank">www.ttic.edu</a> </font></span></div><div><span style="background-color:rgb(255,255,255)"><font color="#3d85c6"><br></font></span></div></div></div></div>
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