15 accepted papers
With the advance of federated learning (FL) in privacy-sensitive domains such as healthcare, finance, and mobile intelligence, the need for efficient and robust training becomes increasingly urgent. Communication bottlenecks, heterogeneous client distributions, and fairness requirements make it esse…
Auction-based Federated Learning (AFL) fosters collaboration among self-interested data consumers (DCs) and data owners (DOs). A major challenge in AFL pertains to how DCs select and bid for DOs. Existing methods are generally static, making them ill-suited for dynamic AFL markets. To address this i…
Federated Active Learning (FAL) aims to learn an effective global model, while minimizing label queries. Owing to privacy requirements, it is challenging to design effective active data selection schemes due to the lack of cross-client query information. In this paper, we bridge this important gap b…
Auction-based Federated Learning (AFL) has emerged as an important research field in recent years. The prevailing strategies for FL data consumers (DCs) assume that the entire team of the required data owners (DOs) for an FL task must be assembled before training can commence. In practice, a DC can…
Auction-based Federated Learning (AFL) has gained significant research interest due to its ability to incentivize data owners (DOs) to participate in FL model training tasks of data consumers (DCs) through economic mechanisms via the auctioneer. One of the critical research issues in AFL is decision…
Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leadin…
Active Noise Control (ANC) over large regions of interest (ROI) traditionally requires numerous evenly distributed error microphones, which is often impractical and obstructive for human occupants. In this paper, we proposed a wave domain adaptive ANC algorithm using the joint information from multi…
Personalized federated learning (PFL) is designed for scenarios with non-independent and identically distributed (non-IID) client data. Existing model mixup-based methods, one of the main approaches of PFL, can only extract either global or personalized features during training, thereby limiting eff…
Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregati…
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their dat…
Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimatio…
Social insurance benefits qualification assessment is an important task to ensure that retirees enjoy their benefits according to the regulations. It also plays a key role in curbing social security frauds. In this paper, we report the deployment of the Intelligent Benefit Certification and Analysis…
Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for…
Auction-based federated learning (AFL) is an important area of FL incentive mechanism design. It effectively incentivizes high-quality data owners (DOs) to participate in data consumers' (DCs, i.e., servers') FL training tasks. However, AFL is still evolving, with existing methods primarily addressi…
Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. Existing AFL approaches cannot manage the mutual influence among multiple data consumers competing to enlist data owners. Moreover, they cannot support a single data owner to join…