AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning

Deng, Yongheng; Lyu, Feng; Ren, Ju; Wu, Huaqing; Zhou, Yuezhi; Zhang, Yaoxue; Shen, Xuemin

doi:10.1109/tpds.2021.3134647

Cited by 121 publications

(27 citation statements)

References 31 publications

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“…Resource allocation methods employ frequency scheduling [18], transmission power control [19], and bandwidth allocation [20] to balance the cost of local training. Recent device selection methods directly exclude those weak devices with poor computation or communication capabilities to accelerate the convergence time [21]- [23]. Besides, topology-aware management is another very effective method to mitigate the network throughput [18], [24], [25].…”

Section: Related Workmentioning

confidence: 99%

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Li¹,

Cheng²,

Huang³

et al. 2023

Preprint

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In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on Any-costFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-theart efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

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Section: Related Workmentioning

confidence: 99%

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Li¹,

Cheng²,

Huang³

et al. 2023

Preprint

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“…This hyperparameter optimization is difficult to achieve on a federated medical dataset since the clients do not want to participate and contribute before benefiting from the FL algorithm through their involvement in the system. One of the possible ways to achieve this is to design an appropriate auction mechanism with incentives that would motivate the clients to contribute toward hyperparameter optimization [238].…”

Section: A Hyperparameter Optimization For Efficient Fl Systemsmentioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

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“…Meanwhile, the ever-growing concerns of data privacy and security also call for feasible and flexible distributed ML mechanisms. Regarding this backdrop, the interesting concept of federated learning (FL), firstly provided by Google, offers a privacy-preserving distributed ML paradigm and has attracted extensive attention from both industry and academia alike [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the model parameters (or gradients) are uploaded to the aggregator synchronously (or asynchronously) and aggregated as a new global model via a specific aggregation algorithm, e.g., FedAvg [9]. Recently, extensive efforts are devoted to different aspects of FL, e.g., client selection [6], [10], resource management [11], [12], architecture design [13], [14] and incentive mechanism design [7], [15]. Although existing works have made lots of contributions, most of them focus on the single FL service scenario, while neglecting multiple co-existing FL services.…”

Section: Introductionmentioning

confidence: 99%

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Cheng

Liwang

Xia

et al. 2022

IEEE Trans. Veh. Technol.

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Federated learning (FL) represents a promising distributed machine learning paradigm that allows smart devices to collaboratively train a shared model via providing local data sets. However, problems considering multiple co-existing FL services and different types of service providers are rarely studied. In this paper, we investigate a multiple FL service trading problem in Unmanned Aerial Vehicle (UAV)-aided networks, where FL service demanders (FLSDs) aim to purchase various data sets from feasible clients (smart devices, e.g., smartphones, smart vehicles), and model aggregation services from UAVs, to fulfill their requirements. An auction-based trading market is established to facilitate the trading among three parties, i.e., FLSDs acting as buyers, distributed located client groups acting as data-sellers, and UAVs acting as UAV-sellers. The proposed auction is formalized as a 0-1 integer programming problem, aiming to maximize the overall buyers' revenue via investigating winner determination and payment rule design. Specifically, since two seller types (data-sellers and UAV-sellers) are considered, an interesting idea integrating seller pair and joint bid is introduced, which turns diverse sellers into virtual seller pairs. Vickrey-Clarke-Groves (VCG)-based, and one-sided matchingbased mechanisms are proposed, respectively, where the former achieves the optimal solutions, which, however, is computationally intractable. While the latter can obtain suboptimal solutions that approach to the optimal ones, with low computational complexity, especially upon considering a large number of participants. Significant properties such as truthfulness and individual rationality are comprehensively analyzed for both mechanisms. Extensive experimental results verify the properties and demonstrate that our proposed mechanisms outperform representative methods significantly.

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AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning

Cited by 121 publications

References 31 publications

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

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