DFLStar: A Decentralized Federated Learning Framework with Self-Knowledge Distillation and Participant Selection

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

Privacy-preserving Federated Learning (FL) based on Differential Privacy (DP) protects clients’ data by adding DP noise to samples’ gradients and has emerged as a de facto standard for data privacy in FL. However, the accuracy of global models in DP-based FL may be reduced significantly when rogue clients occur who deviate from the preset DP-based FL approaches and selfishly inject excessive DP noise beyond expectations, thereby applying a smaller privacy budget in the DP mechanism to ensure a higher level of security. Existing DP-based FL fails to prevent such attacks as they are imperceptible. Under the DP-based FL system and random Gaussian noise, the local model parameters of the rogue clients and the honest clients have identical distributions. In particular, the rogue local models show a low performance, but directly filtering out lower-performance local models compromises the generalizability of global models, as local models trained on scarce data also behave with low performance in the early epoch. In this paper, we propose ReFL, a novel privacy-preserving FL system that enforces DP and avoids the accuracy reduction of global models caused by excessive DP noise of rogue clients. Based on the observation that rogue local models with excessive DP noise and honest local models trained on scarce data have different performance patterns in long-term training epochs, we propose a long-term contribution incentives scheme to evaluate clients’ reputations and identify rogue clients. Furthermore, we design a reputation-based aggregation to avoid the damage of rogue clients’ models on the global model accuracy, based on the incentive reputation. Extensive experiments demonstrate ReFL guarantees the global model accuracy performance 0.77% - 81.71% higher than existing DP-based FL methods in the presence of rogue clients.

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS frameworks and enable privacy-preserving collaborative learning schemes in decentralized ecosystems. However, the vanilla FL system utilizes a centralized FL aggregation technique, which introduces a single point of failure (SPoF) and potential unauthorized aggregators, allowing malicious clients to inject false data parameters, known as poisoning attacks. Furthermore, low-quality clients in the FL system can result in degraded model performance and hinder convergence. This study proposes a secure and reliable blockchain-aided federated learning (BFL)-based IDS framework using a lightweight model for securing metaverse. An authorized federated IDS is proposed to establish a trustworthy decentralized aggregation mechanism, utilizing proof-of-authority (PoA) consensus. The proposed federated IDS implemented a hybrid client selection (HCS) technique, considering the accuracy and reputation of client histories, to select high-quality metaverse edge devices. Additionally, a fairness ERC-20 token-based incentive mechanism was developed to reward selected FL clients as a token of appreciation for their contribution to the FL training processes. According to the IDS framework measurements, the proposed model performs better than the existing approaches for detecting cyber-attacks in metaverse environments, achieving an accuracy of 99.28% with trainable parameters of 1.8K and mega floating-point operations (MFLOPs) of 0.0016.

In federated learning (FL), clients collectively train a global machine learning model with their own local data. Without sharing sensitive raw data, each client in FL only sends updated weights to consider privacy and security concerns. Most of existing FL works focus mainly on improving model accuracy and training time, but only a few works focus on FL incentive mechanisms. To build a high performance model after FL training, clients need to provide high quality and large amounts of data. However, in real FL scenarios, high-quality clients are reluctant to participate in FL process without reasonable compensation, because clients are self-interested and other clients can be business competitors. Even participation incurs some cost for contributing to the FL model with their local dataset. To address this problem, we propose TIFF, a novel tokenized incentive mechanism, where tokens are used as a means of paying for the services of providing participants and the training infrastructure. Without payment delays, participation can be monetized as both providers and consumers, which promotes continued long-term participation of high-quality data parties. Additionally, paid tokens are reimbursed to each client as consumers according to our newly proposed metrics (such as token reduction ratio and utility improvement ratio), which keeps clients engaged in FL process as consumers. To measure data quality, accuracy is calculated in training without additional overheads. We leverage historical accuracy records and random exploration to select high-utility participants and to prevent overfitting. Results show that TIFF provides more tokens to normal providers by up to 6.9% and less tokens to malicious providers by up to 18.1%, achieving improvement of the final model accuracy by up to 7.4%, compared to the default approach.

Artificial Intelligence (AI) systems based on traditional Deep Learning (DL) are expected to play a leading role in the early detection of various diseases in healthcare applications. However, there are two major drawbacks of these systems: protecting patient privacy and obtaining sufficiently large, high-quality datasets to train reliable models. In traditional DL, collecting data from different sources on a single central server increases system complexity and raises serious privacy and security concerns. Federated Learning (FL) makes it possible to train models locally at multiple data locations while collaboratively improving a global model without exposing raw data, making it a promising architectural solution for privacy preservation. Although previous studies have reported that FL can achieve performance comparable to centralized DL approaches, traditional FL approaches often struggle to maintain consistent performance across different settings. This limitation becomes more noticeable when heterogeneous data distributions, modalities, and domains are involved. In these situations, client drift, overfitting, and generalization capability of the global model arise as major challenges. Thus, this study presents FedCycle as an incremental improvement of the FedAvg algorithm. It modifies the aggregation frequency. It aims to overcome these drawbacks and make the global model more stable and efficient. The FedCycle eliminates centralized data collection, enhances data security, and effectively reduces client drift and overfitting by supporting model training across heterogeneous data distributions, modalities, and domains. The performance evaluation involves extensive experiments using various real-world breast cancer image datasets, namely BREAKHIS, ROBOFLOW, RSNA, BUSI, and BCFPP. The presented method is evaluated against both traditional DL and FL approaches using accuracy, precision, recall, F1-score, and AUC. The findings confirm that applying fine-tuning within FedCycle reduces overfitting during training. As a result, FedCycle achieves performance improvements of 7.75% and 4.65% in accuracy and F1-score on the RSNA and BCFPP datasets compared to traditional DL approaches, while also providing an average improvement of approximately 1.5% in accuracy and F1-score across BREAKHIS, ROBOFLOW, and BUSI datasets compared to FedAvg.

Federated learning (FL) is an effective distributed learning paradigm for protecting client privacy, enabling multiple clients to collaboratively train a global model without uploading private data. It has promising applications in sports image classification. However, FL faces the issue of non-independent and identically distributed (non-IID) data, which leads to excessive variance between local models and hinders the convergence of the global model. Although FedSAM and its variants attempt to reduce this variance by finding smooth solutions between local models, local smoothing does not necessarily result in global smoothing. We refer to this issue as the smoothness inconsistency problem. To address this challenge, we propose a novel FL paradigm, named A-FedSAM, which utilizes adaptive local distillation to achieve consistency in smoothing between local and global models without incurring additional communication overhead, thereby improving the convergence accuracy of the global model. Specifically, A-FedSAM employs the global model as the teacher during local training, dynamically guiding the local models to ensure that their gradients not only maintain smoothness but also align with the global objective. Extensive experiments on sports image classification tasks demonstrate that A-FedSAM outperforms state-of-the-art methods in terms of accuracy across different data heterogeneities and client sampling rates, while requiring fewer communication and computational resources to achieve the same target accuracy.

Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of failure. To address these issues, the Decentralized FL (DFL) paradigm has been proposed, which removes the client–server boundary and enables all participants to engage in model training and aggregation tasks. Nevertheless, as CFL, DFL remains vulnerable to adversarial attacks, notably poisoning attacks that undermine model performance. While existing research on model robustness has predominantly focused on CFL, there is a noteworthy gap in understanding the model robustness of the DFL paradigm. In this paper, a thorough review of poisoning attacks targeting the model robustness in DFL systems, as well as their corresponding countermeasures, are presented. Additionally, a solution called DART is proposed to evaluate the robustness of DFL models, which is implemented and integrated into a DFL platform. Through extensive experiments, this paper compares the behavior of CFL and DFL under diverse poisoning attacks, pinpointing key factors affecting attack spread and effectiveness within the DFL. It also evaluates the performance of different defense mechanisms and investigates whether defense mechanisms designed for CFL are compatible with DFL. The empirical results provide insights into research challenges and suggest ways to improve the robustness of DFL models for future research.

Medical institutions often revoke data access due to the privacy concern of patients. Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased global model based on collecting updates from local models trained by client’s data while keeping the local data private. This study aims to address the centralized data collection issue through the application of FL on brain tumor identification from MRI images. At first, several CNN models were trained using the MRI data and the best three performing CNN models were selected to form different variants of ensemble classifiers. Afterward, the FL model was constructed using the ensemble architecture. It was trained using model weights from the local model without sharing the client’s data (MRI images) using the FL approach. Experimental results show only a slight decline in the performance of the FL approach as it achieved 91.05% accuracy compared to the 96.68% accuracy of the base ensemble model. Additionally, same approach was taken for another slightly larger dataset to prove the scalability of the method. This study shows that the FL approach can achieve privacy-protected tumor classification from MRI images without compromising much accuracy compared to the traditional deep learning approach.

Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of clients and its security can be strengthened by cryptographic methods such as additively homomorphic encryption (HE). However, the efficiency of FL could seriously suffer from the statistical heterogeneity in both the data distribution discrepancy among clients and the global distribution skewness. We mathematically demonstrate the cause of performance degradation in FL and examine the performance of FL over various datasets. To tackle the statistical heterogeneity problem, we propose a pluggable system-level client selection method named Dubhe, which allows clients to proactively participate in training, meanwhile preserving their privacy with the assistance of HE. Experimental results show that Dubhe is comparable with the optimal greedy method on the classification accuracy, with negligible encryption and communication overhead.

Federated learning (FL) has been proposed to enable distributed learning on artificial intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Since random initial models in FL can easily result in unregulated stochastic gradient descent (SGD) processes, existing FL methods greatly suffer from both slow convergence and poor accuracy, especially in non-IID scenarios. To address this problem, we propose a novel method named CyclicFL, which can quickly derive effective initial models to guide the SGD processes, thus improving the overall FL training performance. We formally analyze the significance of data consistency between the pre-training and training stages of CyclicFL, showing the limited Lipschitzness of loss for the pre-trained models by CyclicFL. Moreover, we systematically prove that our method can achieve a faster convergence speed under various convexity assumptions. Unlike traditional centralized pre-training methods that require public proxy data, CyclicFL pre-trains initial models on selected AIoT devices cyclically without exposing their local data. Therefore, they can be easily integrated into any security-critical FL methods. Comprehensive experimental results show that CyclicFL can not only improve maximum classification accuracy by up to 14.11%, but also significantly accelerate the overall FL training process.

In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data. While FL ensures data privacy through collaborative learning, it faces several critical challenges. These include vulnerabilities to reverse engineering, risks to model architecture privacy, susceptibility to model poisoning attacks, threats to data integrity, and the high costs associated with communication and connectivity. This paper presents a comprehensive review of FL, categorizing data partitioning formats into horizontal federated learning, vertical federated learning, and federated transfer learning. Furthermore, it explores the integration of FL with blockchain, leveraging blockchain’s decentralized nature to enhance FL’s security, reliability, and performance. The study reviews existing FL models, identifying key challenges such as privacy risks, communication overhead, model poisoning vulnerabilities, and ethical dilemmas. It evaluates privacy-preserving mechanisms and security strategies in FL, particularly those enabled by blockchain, such as cryptographic methods, decentralized consensus protocols, and tamper-proof data logging. Additionally, the research analyzes regulatory and ethical considerations for adopting blockchain-based FL solutions. Key findings highlight the effectiveness of blockchain in addressing FL challenges, particularly in mitigating model poisoning, ensuring data integrity, and reducing communication costs. The paper concludes with future directions for integrating blockchain and FL, emphasizing areas such as interoperability, lightweight consensus mechanisms, and regulatory compliance.

Federated learning (FL) enables multiple clients to collaboratively train a globally generalized model while keeping local data decentralized. A key challenge in FL is to handle the heterogeneity of data distributions among clients. The local model will shift the global feature when fitting local data, which results in forgetting the global knowledge. Following the idea of knowledge distillation, the global model's prediction can be utilized to help local models preserve the global knowledge in FL. However, when the global model hasn't converged completely, its predictions tend to be less reliable on certain classes, which may results in distillation's misleading of local models. In this paper, we propose a class-wise adaptive self distillation (FedCAD) mechanism to ameliorate this problem. We design class-wise adaptive terms to soften the influence of distillation loss according to the global model's performance on each class and therefore avoid the misleading. Experiments show that our method outperforms other state-of-the-art FL algorithms on benchmark datasets.

Federated learning (FL) is a promising machine learning paradigm that focuses on sending data to the client, then using the local unique data to train the local model. Only the model updates are sent back to be aggregated into a global model; this action can efficiently protect the data privacy and security. However, developing the FL model in the real world is still unreliable due to the problem that the real-world dataset always contains both desired in-distribution data and unexpected out-of-distribution (OOD) data. Current FL researchers typically use prefabricated artificial data sets to conduct a series of experiments and obtain positive results, yet most of these datasets are in-distribution data. In this work, we propose a light weight Client-Side OOD Filtering method tailored for real-world FL compatibility. This method integrates seamlessly with standard FL frameworks by adding a simple pre-processing step on the client side, without modifying server-side aggregation logic. The core design includes two key components: (1) A lightweight ID classifier that quantifies the alignment between local data and ID distribution with soft max probability. (2) An adaptive threshold mechanism that retains data with probability ≥ IID data for regional training and filters out low probability data.

Federated learning effectively addresses the issues of data privacy and communication overhead in traditional deep learning through distributed local training. However, its open architecture is seriously threatened by backdoor attacks, where malicious clients can implant triggers to control the global model. To address these issues, this paper proposes a novel three-stage defense mechanism based on local clients. First, through text readability analysis, each client’s local data is independently evaluated to construct a global scoring distribution model, and a dynamic threshold is used to precisely locate and remove suspicious samples with low readability. Second, frequency analysis and perturbation are performed on the remaining data to identify and disrupt triggers based on specific words while preserving the basic semantics of the text. Third, n-gram distribution analysis is employed to detect and remove samples containing abnormally high-frequency word sequences, which may correspond to complex backdoor attack patterns. Experimental results show that this method can effectively defend against various backdoor attacks with minimal impact on model accuracy, providing a new solution for the security of federated learning.