Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upadhye, Truptee; Nanjundan, Preethi Title A name given to the resource Federated Multi-Task Learning Using Server-Side Normalized Loss-Based Weighting Method Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE Pune Section International Conference, PuneCon 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/PuneCon67554.2025.11378701" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/PuneCon67554.2025.11378701</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034681230?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034681230?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upadhye T., Christ University, Department of Data Science, Bangalore, India; Nanjundan P., Christ University, Department of Data Science, Bangalore, India Description An account of the resource Federated multi-task learning is an approach where multiple clients collaboratively train related but distinct models on their local data without sharing it, thereby preserving privacy while leveraging collective knowledge. However, participating clients can have very different data distributions, sizes and quality, leading to statistical heterogeneity. This heterogeneity is a major challenge in federated learning, as noisy or inconsistent updates from some clients can slow down convergence or degrade the global model's performance. MOCHA is a seminal federated multi-task learning framework that explicitly models task relationships and optimizes clientspecific models, while addressing system challenges like communication costs, fault tolerance and client dropouts. In this work, we enhance MOCHA with a server-side normalized lossbased weighting technique that focuses on the quality of client updates. Each client in the federated multi-task setup computes its local training loss, which is sent to the server during communication rounds. The server normalizes these losses across clients and assigns adaptive aggregation weights, giving more influence to clients with lower normalized losses and down-weighting noisy or unreliable clients. This design simplifies client-side implementation because all weighting is performed at the server. Experiments on heterogeneous MNIST and CIFAR-10 tasks show that the proposed method achieves a slightly higher final-round average test accuracy (0.5108 vs. 0.5065), reduces average training loss by approximately 2.6% (from 1.1148 to 1.0858), and improves fairness by lowering the standard deviation of client accuracies by about 5% (from 0.3631 to 0.3450) compared to baseline MOCHA. These results indicate that server-side normalized loss-based weighting improves training stability, convergence behavior and crossclient fairness in federated multi-task learning under nonconvex optimization. 2025 IEEE. Subject The topic of the resource convergence speed; data distributions; decentralized learning; Federated multi-task learning; MOCHA algorithm; nonconvex optimization; server-side normalized loss-based weighting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158834-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online