Incentivizing data owners to contribute to federated learning (FL) is crucial to the sustainable operation of an FL ecosystem. Existing incentive mechanisms are designed assuming that all data owners are known to FL task publishers, which may not always hold in practical scenarios. As the domain of auction-based FL (AFL) continues to grow in importance, we are rethinking this assumption to better suit the realities of Federated Auctioning Networks (FANs). We propose an incentive mechanism named FIM-FAN, which is designed to perform data owner selection based on Lyapunov optimization through referrals among data owners in FANs, without relying on the existence of an entity that has information about all data owners. In FIM-FAN, a data owner can perform the dual roles of a worker and a referrer. Both activities can be incentivized monetarily. To solve the data owner selection problem, an online greedy client selection algorithm is proposed considering reputation, bidding price, and fairness. Reputation is also involved in the compensation calculation process to encourage honest behaviors in FAN. Theoretical analysis shows that FIM-FAN satisfies budget feasibility and individual rationality. Extensive experiments on MNIST and CIFAR-10 datasets against 10 baselines demonstrate that, on average, FIM-FAN outperforms them by 6.54% and 13.63% in terms of test accuracy and fairness, respectively.

Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research attention. However, the problem of optimizing FL client selection in mobile federated learning networks (MFLNs), where devices move in and out of each others’ coverage and no FL server knows all the data owners, remains open. To bridge this gap, we propose a first-of-its-kind Socially-aware Federated Client Selection (SocFedCS) approach to minimize costs and train high-quality FL models. SocFedCS enriches the candidate FL client pool by enabling data owners to propagate FL task information through their local networks of trust, even as devices are moving into and out of each others’ coverage. Based on Lyapunov optimization, we first transform this time-coupled problem into a step-by-step optimization problem. Then, we design a method based on alternating minimization and self-adaptive global best harmony search to solve this mixed-integer optimization problem. Extensive experiments comparing SocFedCS against five state-of-the-art approaches based on four real-world multimedia datasets demonstrate that it achieves 2.06% higher test accuracy and 12.24% lower cost on average than the best-performing baseline.

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants' contribution to model performance without exposing their private data, and improves the FL model training protocol to allow the best performing intermediate models to be distributed to participants for FL training. Since its deployment in Yidu Cloud Technology Inc. in March 2021, CAFL has served 8 well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations 2.84 times faster than the best existing approach, and has improved the average accuracy of the resulting models by 2.62% compared to the previous system (which is significant in industrial settings). To our knowledge, it is the first contribution-aware federated learning successfully deployed in the healthcare industry.

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FEDOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FEDOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.

Smartphones have become a part of everyday life, and users are contributing to Machine Learning with a simple touch (ML). Federated Learning (FL) is a new collaborative learning technique that preserves privacy and addresses the problem of traditional ML. Despite this, it has a large attack surface area and is vulnerable to privacy attacks. Studying the impact of such attacks on the resulting FL models is an important research topic. Currently, there is a lack of an experimental platform to conduct such studies. We attempt to bridge this gap in this paper by proposing the Federated Learning Attack Simulation (FLAS) platform. It is a web-based application designed with an easy-to-use workflow for non-experts and the ability to accelerate testing and analysis for Federated Learning (FL) professionals. Preliminary evaluations have demonstrated the effectiveness of FLAS in supporting the study of common privacy attacks on FL.