Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for
data is growing. Much of this data is private and distributed across edge devices, making …

The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of
Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model …

In modern recommender system applications, such as e-commerce, predicting multiple
targets like click-through rate (CTR) and post-view click-through\& conversion rate (CTCVR) …

In practical scenarios, federated learning frequently necessitates training personalized
models for each client using heterogeneous data. This paper proposes a backbone self …

We address the problem of cluster identity estimation in a hierarchical federated learning
setting in which users work toward learning different tasks. To overcome the challenge of …

A fundamental challenge in federated learning lies in mixing heterogeneous datasets and
classification tasks while minimizing the high communication cost caused by clients as well …

This study presents the Task-level Heterogeneous Federated Learning (TH-FL), a novel
paradigm that fuses the principles of Federated Learning (FL) and Multi-Task Learning …

In the evolving landscape of federated learning (FL), the integration of multimodal data
presents both unprecedented opportunities and significant challenges. Existing work falls …