Fair and Efficient Federated Learning Client Selection via Dynamic Contribution Evaluation

Federated learning is a distributed machine learning paradigm that allows multiple edge devices to collaboratively train a shared model without exchanging raw data. However, the training efficiency of federated learning is highly dependent on client selection. Moreover, due to the varying wireless communication environments and various computation latencies among the clients, selecting clients randomly or uniformly may not be optimal for balancing the data diversity and training efficiency. In this article, we formulate a new latency-minimization problem that simultaneously optimizes client selection and training procedures in federated learning, which takes into account the data and latency heterogeneity among the clients. Given the nonconvexity of the problem, we derive a new convergence upper bound for federated learning with probabilistic client selection. To solve the mixed integer nonlinear programming problem, we introduce a hybrid solution that integrates grid search techniques with the polyhedral active set algorithm. Numerical analyses and experiments on real-world data demonstrate that our scheme outperforms the existing ones in terms of overall training latency and achieves up to three times acceleration over random client selection, especially in scenarios with highly heterogeneous data and latencies among the clients.

Hierarchical federated learning (HFL) has recently emerged as a more practical machine learning (ML) paradigm, which enables edge servers (ESs) in close proximity to conduct partial model aggregation. Despite its utility, local training and model aggregation incur considerable computation and communication time. client selection (CS) has proven effective for minimizing latency. However, CS faces the following challenges in hierarchical federated learning (HFL). First, the accessible clients, computation resources and network bandwidth are time-varying and unpredictable. Second, certain dynamics can only be observed after the decisions are made. Third, multiple ESs face different unknown clients, increasing the difficulty of selecting clients in an online manner. Finally, resource usage may be excessively violated during the training process. Existing HFL researches are insufficient to tackle these challenges. This work proposes a multi- ESs CS framework (MCS), which is based on multiarmed bandit (MAB) technique. MCS aims to reduce the cumulative computation and communication time, using two algorithms: 1) an online learning-based CS algorithm (OCA) makes the CS decisions for each ES, based on empirical learning results; and 2) a randomized rounding algorithm (RRA) converts fractional decisions obtained by OCA into binary solutions. Theoretically, MCS can enjoy the sublinear regret and violation compared to the optimal strategy. Practically, extensive experiments on real-world data sets demonstrate the empirical superiority of MCS over multiple state-of-the-art algorithms in minimizing cumulative latency.

Federated learning (FL) represents a transformative shift in machine learning, moving from conventional centralized approaches to a distributed framework that emphasizes data privacy and security. The FL server transmits an initial model to clients, which they train locally on their private data. After training, the server aggregates model updates from each client to update the global model. Selecting the best clients in FL is critical to improving the convergence speed and accuracy of the final model, which requires careful client selection approaches. The client selection phase of FL faces numerous challenges that impact overall training performance, including statistical heterogeneity and system heterogeneity resulting from the diversity of client data and resources. Communication costs present another challenge, especially in networks with limited client communication resources. Additionally, selecting trustworthy clients represents another challenge, as selecting malicious clients creates a significant risk within the FL training process. Moreover, the fairness challenge entails providing fair opportunities for all clients to participate in training. To address these challenges, we offer solutions that utilize effective techniques, approaches, and client selection methods. This survey presents a taxonomy of modern client selection methods in FL, highlighting the improvements in FL performance and effectiveness achieved through these methods, including greedy selection, reinforcement learning-based selection, multi-armed bandit-based selection, clustering-based selection, and reputation & security-based selection. Subsequently, it offers a general comparison between these methods in terms of their core ideas, advantages, limitations, and use cases. Finally, future and potential trends in client selection and improving performance in FL are identified.

Federated Learning (FL) has gained significant attention as a distributed machine learning paradigm, leveraging edge clients for collaborative training while preserving data privacy. However, statistical heterogeneity, arising from diverse client data distributions, poses a significant challenge to FL performance. In this work, we propose a novel heterogeneity-aware client selection scheme based on prototype margin to mitigate these challenges. By utilizing prototype learning, we capture effective data representations, allowing client selection to better account for both data quality and distribution. The proposed scheme is designed for practical wireless communication networks, where clients are clustered and we select participants based on joint prototype margin and wireless channel. To further optimize the training process, we jointly optimize client selection and bandwidth allocation, accounting for dynamic network conditions and heterogeneous client capabilities. Experimental results demonstrate that our approach significantly improves model accuracy and reduces communication overhead.

Federated learning (FL) enables privacy-preserving distributed machine learning by training models on edge client devices using their local data without revealing their raw data. In edge environments, various applications require different neural network models, making it crucial to perform joint training of multiple models on edge devices, known as multi-task FL. While existing multi-task FL approaches enhance resource utilization on edge devices through adaptive resource configuration or client selection, optimizing either of these aspects alone may lead to suboptimality. Therefore, in this paper, we explore a joint client selection and resource configuration method called JCSRC for multi-task FL, aiming to maximize energy efficiency in environments with limited computation and communication resources and heterogeneous client devices. Firstly, we formalize this problem as a mixed-integer nonlinear programming problem considering all these characteristics and prove its NP-hardness. To address this problem, we first design a multi-agent reinforcement learning (MARL)-based client selection method that selects appropriate clients for each task to train their models. The MARL method makes client selection decisions based on the clients’ data quality, energy efficiency, communication, and computation capacity to ensure fast convergence and energy efficiency. Then, we design a particle swarm optimization (PSO)-based resource configuration scheme that configures appropriate computation and bandwidth resources for each task on each client. The PSO scheme makes resource configuration decisions based on theoretically derived optimal CPU frequency and bandwidth to achieve high energy efficiency. Finally, we carry out extensive simulations and testbed-based experiments to validate our proposed JCSRC. The results demonstrate that, in comparison to state-of-the-art solutions, JCSRC can save energy consumption by up to 59% to achieve the target accuracy.

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critical factor for the performance of the overall system. In this survey, we provide a comprehensive overview of the state-of-the-art client selection techniques in FL, including their strengths and limitations, as well as the challenges and open issues that need to be addressed. We cover conventional selection techniques such as random selection where all or partial random of clients is used for the trained. We also cover performance-aware selections and as well as resource-aware selections for resource-constrained networks and heterogeneous networks. We also discuss the usage of client selection in model security enhancement. Lastly, we discuss open issues and challenges related to clients selection in dynamic constrained, and heterogeneous networks.

Utilizing the federated learning (FL) technique, data owners can collaboratively train artificial intelligence models, retaining all training data on their premises to minimize the potential for personal data breaches. However, self-interested users (e.g., free riders) bring new challenges that hinder the development of FL techniques. To this end, we propose a two-stage client selection scheme comprising a multiarmed bandit (MAB)-based candidate client selection method and an auction-based training client selection method. Specifically, our client selection scheme initially formulates the FL system into an MAB system, where clients are the arms and the server is the player. Then, we quantify the similarity between a local model and the server side, which is the designed metric for model aggregation and reward computation updating based on the fuzzy mathematical strategy. Next, based on the Thompson Sampling strategy, the server can intelligently determine the reward of each client, and clients with more significant rewards have the chance for local model training. With an auction method, the server can determine the training clients to reduce the training cost while maximizing each client’s revenue. Extensive experiments on real-world data sets demonstrate that the proposed scheme outperforms representative FL schemes (i.e., FedAvg, FedProx, FedMax, and MFL) regarding the model’s convergence rate and cost in FL systems with free riders.

PurposeThis article addresses recent calls in the literature for advancing our understanding of public affairs consultants and their role conceptions. By testing and further exploring self-perceptions of public affairs consultants the study aims to offer new insight into how consultants define and view their occupational role.Design/methodology/approachThe study draws on a nationwide survey with public affairs consultants in Sweden.FindingsFour main role conceptions were identified (advocate, do-gooder, expert and intermediary). Further, the study tests how personal and professional characteristics correlate with different role conceptions, by viewing professional experience and consultants' selection of clients. Data also suggest that consultants' background in politics does not promote any specific role perception. Finally, the findings also show that how consultants choose clients is a divider in the industry, where some act as passive intermediaries while other take a more active role in their choice of clients.Originality/valueThe findings enhance our understanding of public affairs as a field, and specifically about the modelling of professional roles amongst consultants. The empirical results in this study show how contemporary role typologies needs to be extended to better capture the specificities of consultants' roles in public affairs. By addressing the issue of how consultants choose clients the study engages with the complex debate of whether consultants ought to act as objective or subjective agents and hence join the conversation on ethics in public affairs.

The challenges of distributed computing and privacy preservation in the Internet of Things (IoT) can be effectively addressed through the federated learning (FL) paradigm. A pivotal aspect of this approach is client selection, which plays a crucial role in identifying participating clients while balancing model accuracy and communication efficiency. However, existing methods often have limitations, such as an inability to quickly and accurately assess data quality on the client side and a failure to account for the interdependence among clients by treating each as an independent contributor. To address these issues, we introduce GPFL, a novel framework for client selection in FL. We propose an indicator that rapidly and accurately measures the contribution of heterogeneous client data to FL. Additionally, we design an exploration–exploitation mechanism that selects the optimal combination of clients while considering their interdependencies. Both theoretical and experimental analyzes demonstrate the effectiveness of GPFL in improving FL efficiency and accuracy while preserving data privacy. Experiments on federated extended MNIST (FEMNIST) and CIFAR-10 show that GPFL outperforms baseline methods in Non-IID scenarios, achieving an average improvement of over 9% in FEMNIST accuracy compared to SOTA preselection methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://figshare.com/articles/dataset/PBFL/29264663</uri>.

The introduction of artificial intelligence (AI) tech-nology to Internet of Vehicles (IoV) brings the promising potential of enabling intelligent services to moving vehicles. However, training of conventional centralized AI model suffers from long access latency to cloud server and data privacy leakage of vehicle clients. To address these issues, federated learning (FL) has been introduced to IoV for distributed privacy-preserving model training. Nevertheless, to optimize the system overhead and model accuracy of FL, the selection of federated clients has become a critical problem. To overcome this challenge, a proximal policy optimization (PPO)-based client selection scheme has been proposed in this work for the federated AI model training in IoV. Specifically, the federated client selection problem is formulated as a Markov decision process (MDP) first, and a PPO-based algorithm has been developed for the MDP. Subsequently, the dataflow of the proposed client selection scheme is presented in detail. Simulations have been conducted based on practical scenario settings. Simulation results indicate that the proposed client selection scheme outperforms the double deep Q network (DDQN)-based scheme and biased selection scheme on the con-vergence time and test accuracy of federated model training with more stable system performance.

Federated Learning (FL) has shown great potential as a privacy-preserving solution to training a centralized model based on local data from available clients. However, we argue that, over the course of training, the available clients may exhibit some volatility in terms of the client population, client data, and training status. Considering these volatilities, we propose a new learning scenario termed volatile federated learning (Volatile FL) featuring set volatility, statistical volatility, and training volatility. The volatile client set along with the dynamic of clients’ data and the unreliable nature of clients (e.g., unintentional shutdown and network instability) greatly increase the difficulty of client selection. In this paper, we formulate and decompose the global problem into two sub-problems based on alternating minimization. For an efficient settlement for the proposed selection problem, we quantify the impact of clients’ data and resource heterogeneity for Volatile FL and introduce the cumulative effective participation data as an optimization objective. Based on this, we propose Upper Confidence Bound based Greedy Selection, dubbed UCB-GS, to address the client selection problem in volatile FL. Theoretically, we prove that the regret of UCB-GS is strictly bounded by a finite constant, justifying its theoretical feasibility. Furthermore, experimental results show that our method significantly reduces the number of training rounds (by up to 62%) while increasing the global model’s accuracy by 7.51%.&#13;\n

Federated learning (FL) unleashes the full potential of training a global statistical model collaboratively from edge clients. In wireless FL, for the scarcity of spectrum, only a fraction of clients are capable to participate in the FL training in each round. On the other hand, the performance of FL suffers from the label noise, which naturally exists in the dataset of each client. To alleviate the label noise issue, prior works proposed several client selection algorithms, where the privacy of raw data might be compromised when extra information exchange was introduced. To avoid extra information exchange in FL, we propose to leverage an algorithm that measures the noise ratio of each client based on a clean validation dataset. We then propose an online client selection framework, supported by Copeland score and multi-arm bandits, which can select the client set with low noise ratios efficiently. The proposed algorithm is performed in the server side, thereby it can be implemented in the basic FL framework seamlessly. Simulation results demonstrate that our proposed algorithm spends less training time while guaranteeing the required accuracy compared to other baseline algorithms.

The Selection of Clients from the Blind Population

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Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection (CS) problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for CS to minimize the training latency without harming the ability of the model to generalize, i.e., to give reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. Furthermore, simulation results using synthetic and real datasets demonstrate that BSFL is superior to existing methods.