14 accepted papers
Training automatic speech recognition (ASR) models increasingly relies on decentralized federated learning to ensure data privacy and accessibility, producing multiple local models that require effective merging. In hybrid ASR systems, while acoustic models can be merged using established methods, t…
One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server‑side foundation model. Existing methods based on model level or representation level knowledge transfer either require expensive local training or
As publicly available data dwindles, synthetic data generation (SDG) has become a practical solution for privacy-preserving data sharing. By training generative models on private data, SDG creates samples that retain task-relevant features while obfuscating sensitive content. However, recent work sh
This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Specifically, we propose the first method tailored to *label unlearning* in VFL, where labels play a dual role as both essen…
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies on high-fidelity label distributions that are costly to obtain and thus often noisy. Motivated by privacy-sensitive appli…
Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deployment in resource-constrained environments and concerns over user data privacy pose significant challenges. In contrast, Sma
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership Inference Attacks (MIAs), which aim to determine whether a specific data point belongs to a target client's training se…
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLMs to small language models (SLMs) at downstream clients. However, a significant g…
Federated Continual Learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not only spatial data heterogeneity between clients but also temporal data heterogenei…
In this paper, we explore an important yet previously neglected question: Do context aggregation patterns across Language Models (LMs) share commonalities? While some works have investigated context aggregation or attention weights in LMs, they typically focus on individual models or attention heads…
"Federated Class Continual Learning (FCCL) merges the challenges of distributed client learning with the need for seamless adaptation to new classes without forgetting old ones. The key challenge in FCCL is catastrophic forgetting, an issue that has been explored to some extent in Continual Learning…
The advent of Federated Learning (FL) highlights the practical necessity for the ’right to be forgotten’ for all clients, allowing them to request data deletion from the machine learning model’s service provider. This necessity has spurred a growing demand for Federated Unlearning (FU). Feature unle…
In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the "right to be forgotten," the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve a…
Vertical federated learning (VFL) allows an active party with labeled data to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. I…