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Federated Learning (FL) system needs to incentivize clients since they may be reluctant to participate in the resource consuming process. Existing incentive mechanisms fail to construct a sustainable environment for the long-term development of FL system: 1) They seldom focus on system economic properties (e.g., social welfare, individual rationality, and incentive compatibility) to guarantee client attraction. 2) Current online auction modeling methods divide the whole continual process into multiple independent rounds and solve them one-by-one, which breaks the correlation between each round. Besides, the inherent characteristics of FL system (model-agnostic and privacy-sensitive) also prevent it from the optimal strategy by precise mathematical analysis. 3) Current system modelings ignore the practical problem of periodical client shifting, which cannot adaptively update its strategy to handle system dynamics. To overcome the above challenges, this paper proposes a long-term adaptive Vickrey-Clarke-Groves (VCG) auction mechanism for FL system, which incorporate a multi-branch deep reinforcement learning (DRL) algorithm. First, VCG auction is the only one that can simultaneously guarantee all crucial economic properties. Second, we extend the economic properties to long-term forms and apply the experience-driven DRL algorithm to directly obtain long-term optimal strategy, without any prior system knowledge. Third, we reconstruct a multi-branch DRL network to accommodate periodical client shifting by adaptive decision head switching for different time periods. Finally, we theoretically prove he extended economic properties (i.e., IC) and conduct extensive experiments on several real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL system increases by 36% with a 37% reduction in payment. Besides, the multi-branch network can adaptively handle periodical client shifting on the timeline.

Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU method that utilizes dataset distillation (DD) to accelerate unlearning and drastically reduces computational overhead compared to existing approaches. In QuickDrop, each client uses DD to generate a compact dataset representative of the original training dataset, called a distilled dataset, and uses this compact dataset during unlearning. To unlearn specific knowledge from the global model, QuickDrop has clients execute Stochastic Gradient Ascent with samples from the distilled datasets, thus significantly reducing computational overhead compared to conventional FU methods. We further increase the efficiency of QuickDrop by ingeniously integrating DD into the FL training process. By reusing the gradient updates produced during FL training for DD, the overhead of creating distilled datasets becomes close to negligible. Evaluations on three standard datasets show that, with comparable accuracy guarantees, QuickDrop reduces the duration of unlearning by 463.8x compared to model retraining from scratch and 65.1x compared to existing FU approaches. We also demonstrate the scalability of QuickDrop with 100 clients and show its effectiveness while handling multiple unlearning operations.

The Right to be Forgotten gives a data owner the right to revoke their data from an entity storing it. In the context of federated learning, the Right to be Forgotten requires that, in addition to the data itself, any influence of the data on the FL model must disappear, a process we call 'federated unlearning.' The most straightforward and legitimate way to implement federated unlearning is to remove the revoked data and retrain the FL model from scratch. Yet the computational and time overhead associated with fully retraining FL models can be prohibitively expensive. In this article, we take the first step to comprehensively investigate the way to settle the unlearning paradigm in the context of federated learning. First, we define the problem of efficient federated unlearning, including its challenges and goals, and we identify three common types of federated unlearning requests: class unlearning, client unlearning, and sample unlearning. Based on those challenges and goals, a general pipeline is proposed for federated unlearning for the above three types of requests. We revisit how the training data affects the final FL model performance and thereby empowers the proposed framework with the reverse stochastic gradient ascent (SGA) and elastic weight consolidation (EWC). Various experiments are conducted to verify effectiveness of the proposed method in both aspects of unlearning efficacy and efficiency. We believe the proposed method will perform as an essential component for future machine unlearning systems. © 1986-2012 IEEE.

As the large-scale deployment of machine learning applications, there is much research attention on exploiting a vast amount of data stored on mobile clients. To preserve data privacy, federated learning has been proposed to enable large-scale machine learning by massive clients without exposing raw data. Existing works of federated learning struggle for accelerating the learning process, but ignore the energy efficiency that is critical for resource-constrained clients. In this article, we propose to improve the energy efficiency of federated learning by lowering CPU cycle frequencies of clients who are faster in the training group. Based on this idea, we formulate an optimization problem aiming to minimize the total system cost defined as a weighted sum of learning time and energy consumption. Due to the hardness of the formulated optimization problem and unpredictability of network quality, we propose L4L (Learning for Learning), an experience-driven computational resource control approach based on the deep reinforcement learning, which can derive the near-optimal solution with only the clients' bandwidth information in the previous training rounds. We conduct the experiments using both real-world traces and synthetic traces to evaluate the proposed L4L approach. The results demonstrate the superiority of L4L as compared with the state-of-the-art solutions. © 1968-2012 IEEE.

Federated learning is promising in enabling large-scale machine learning by massive clients without exposing their raw data. It can not only enable the clients to preserve the privacy information, but also achieve high learning performance. Existing works of federated learning mainly focus on improving learning performance in terms of model accuracy and learning task completion time. However, in practice, clients are reluctant to participate in the learning process without receiving compensation. Therefore, how to effectively motivate the clients to actively and reliably participate in federated learning is paramount. As compared to the current incentive mechanism design in other fields, such as crowdsourcing, cloud computing, smart grid, etc., the incentive mechanism for federated learning is more challenging. First, it is hard to evaluate the training data value of each client. Second, it is difficult to model the learning performance of different federated learning algorithms. In this article, we survey the incentive mechanism design for federated learning. In particular, we present a taxonomy of existing incentive mechanisms for federated learning, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, some future directions of how to incentivize clients in federated learning have been discussed. © 2013 IEEE.

Federated learning (FL) clients may be reluctant to participate in the energy-consuming FL unless they are incentivized. Existing incentive mechanisms seldom consider the economic properties, e.g., social welfare, individual rationality and incentive compatibility, which significantly limits the sustainability of FL to attract more clients. The Vickrey-Clarke-Groves (VCG) auction is an ideal mechanism for simultaneously guaranteeing all crucial economic properties to maximize social welfare. However, VCG auction cannot be applied directly to FL scenarios due to the following challenges: 1) It requires precise analytical derivation of the optimal strategy, which is unavailable due to the inherent model-unknown and privacy-sensitive characteristics of FL. 2) Current auction modeling decomposes the entire process into multiple independent rounds and solves them one-by-one, which breaks the successive correlation between rounds in the long-term training process of FL. To overcome these challenges, this paper presents a long-term online VCG auction mechanism for FL that employs an experience-driven deep reinforcement learning algorithm to obtain the optimal strategy. Besides, we extend long-term forms of the crucial economic properties for the successive FL process. Furthermore, knowledge transfer is applied to reduce the excessive training overhead arising from the VCG payment rules. By exploiting the environmental similarity among sub-auctions, we develop the strategy sharing to significantly cut the training time by half. Finally, we theoretically prove the extended economic properties and conduct extensive experiments on multiple real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL increases by 36% with a 37% reduction in payment. © 2022 IEEE.

Edge Learning is an emerging distributed machine learning in mobile edge network. Limited works have designed mechanisms to incentivize edge nodes to participate in edge learning. However, their mechanisms only consider myopia optimization on resource consumption, which results in the lack of learning algorithm performance guarantee and longterm sustainability. In this paper, we propose Chiron, an incentive-driven long-term mechanism for edge learning based on hierarchical deep reinforcement learning. First, our optimization goal combines learning-algorithms metric (i.e., model accuracy) with system metric (i.e., learning time, and resource consumption), which can improve edge learning quality under a fixed training budget. Second, we present a two-layer H-DRL design with exterior and inner agents to achieve both long-term and short-term optimization for edge learning, respectively. Finally, experiments on three different real-world datasets are conducted to demonstrate the superiority of our proposed approach. In particular, compared with the state-of-the-art methods under the same budget constraint, the final global model accuracy and time efficiency can be increased by 6.5 % and 39 %, respectively. Our implementation is available at https://github.com/Joey61Liuyi/Chiron. © 2021 IEEE.