New Generation Federated Learning.

Federated Learning (FL) is a paradigm in distributed machine learning, which has gained significant attention in the recent years especially in the domain of Internet of Things. Federated Learning saves communication bandwidth when compared to centralized machine learning process and is also considered to be privacy preserving as the raw data at the clients need not be transmitted to the server for the FL learning. Recent studies on FL have exposed privacy vulnerabilities particularly in the form of Gradient Leakage Attacks (GLA) in which the adversaries can generate training data of the client from shared gradients or model updates from clients. The available solutions in the literature for GLA based on perturbing the gradients from clients leads to a drop in the performance of the FL system while attempting to preserve privacy. We propose HAFedL, an improved novel hessian aware adaptive privacy preserving FL scheme in which the performance of the model is not significantly affected due to the aspects introduced in the FL architecture to improve the privacy of the model against the GLA. The HAFedL is also robust to the data heterogeneity and device heterogeneity (particularly straggler effect) which may be present in the clients participating in the FL. The performance of HAFedL is tested for two applications- IoT device identification and digit classification. The proposed HAFedL scheme can be utilized in privacy sensitive domains such as smart city applications, Industrial Internet of Things, Internet of Robotic Things, Internet of Medical Things, etc.

The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL.

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO.

The exponentially growing Internet of Things (IoT) devices produce gigantic amounts of data every day. To process this ample IoT data, traditional centralized processing methods are mostly used, which face significant latency, bandwidth, and privacy challenges. Federated Learning (FL), a distributed machine learning (ML) technique, has become a promising solution to address these challenges by integrating edge computing environments. Federated edge intelligence can improve model accuracy and data privacy, and reduce latency and bandwidth. This approach allows IoT networks to train ML models collaboratively without centralizing data. This improves efficiency and preserves privacy and security. This chapter presents the fundamentals of FL, its architectural aspects, and demonstrates how it helps to further improve edge intelligence in IoT environments. We also discuss how edge devices can learn and train models with the use of the FL method. Furthermore, we provide examples of practical application use case scenarios, showcasing how this framework enhances real-time decision-making capabilities even while addressing data privacy. Finally, our research aims to guide future scholars through identifying the challenges this architecture currently faces and the future research directions associated with FL-based edge intelligence for IoT.

Federated learning (FL) is one of most promising distributed machine learning frameworks to strengthen data privacy and security by making. Specifically, in the FL, the multiple clients are involved to train their deep learning models (i.e., local training) and then aggregate them as a global model at the server. However, due to the heterogeneous nature of multiple distributed clients, it is challenging to implement and deploy the FL framework over various clients. By adopting the cloud native technologies such as containers, and Kubernetes, we design and implement the Kubernetes enabled Federated Learning Platform (KubeFL). The KubeFL helps the deployment of the FL over multiple clients via Docker containers and Kubernetes. To do this, in the proposed KubeFL, each FL framework implemented by the PyTorch is deployed on the Docker container in the Pod at each client (i.e., Node in the Kubernetes) while the one is also conducted at the server (i.e., Master in the Kubernetes). We evaluate its performance through extensive measurement-based analysis on the commercial NVIDIA Jetson TX2 edge devices under various practical configurations.

Federated learning (FL) is widely used in privacy sensitive applications for isolated data islands, with the aim of achieving distributed model training, privacy enhancement and model sharing. Electromyographic (EMG) signals are a type of data collected from wearable sensors of subjects which are distributed on multiple devices, highly personalized and play an important role in several applications including prosthetic hand control, sign languages, grasp recognition, etc. This paper utilizes the FL method to detect single and combined finger movements based on EMG signals. The existing research on FL for wearable healthcare faces challenges of variable probability distributions of data, the need for prerequisite knowledge of server model and computational burdens in parameter transmission. To address these problems, this paper proposes a communication efficient FL framework in which each device only needs to transmit the weight matrices of local models to the server for model aggregation. To further reduce the FL transmission delay, a joint learning and resource allocation problem is formulated via optimizing transmit power of each device, time allocation, and user selection. To solve the delay minimization problem, the objective function is first converted to a tractable expression and then the difference of two convex functions programming is adopted. Simulation results using real EMG signals show that the proposed FL framework with personalized training process successfully detects single and combined finger movements for distributed users. Two public EMG datasets with 10 and 15 different finger movements are employed. Over 98% overall test accuracy is achieved in both datasets which surpasses the conventional learning framework by 1.6% and 0.5% on average. Different scenarios with regard to access points and users are investigated and the convexity of the proposed model is discussed.

Within the federated learning (FL) framework, the client collaboratively trains the model in coordination with a central server, while the training data can be kept locally on the client. Thus, the FL framework mitigates the privacy disclosure and costs related to conventional centralized machine learning. Nevertheless, current surveys indicate that FL still has problems in terms of communication efficiency and privacy risks. In this paper, to solve these problems, we develop an FL framework with communication-efficient and privacy-preserving (FLCP). To realize the FLCP, we design a novel compression algorithm with efficient communication, namely, adaptive weight compression FedAvg (AWC-FedAvg). On the basis of the non-independent and identically distributed (non-IID) and unbalanced data distribution in FL, a specific compression rate is provided for each client, and homomorphic encryption (HE) and differential privacy (DP) are integrated to provide demonstrable privacy protection and maintain the desirability of the model. Therefore, our proposed FLCP smoothly balances communication efficiency and privacy risks, and we prove its security against “honest-but-curious” servers and extreme collusion under the defined threat model. We evaluate the scheme by comparing it with state-of-the-art results on the MNIST and CIFAR-10 datasets. The results show that the FLCP performs better in terms of training efficiency and model accuracy than the baseline method.

Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the conventional FL framework relies heavily on a single central server, and it may fail if such a server behaves maliciously. To address this single point of failure, in this work, a blockchain-assisted decentralized FL framework is investigated, which can prevent malicious clients from poisoning the learning process, and thus provides a self-motivated and reliable learning environment for clients. In this framework, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. Privacy and resource-allocation issues are further investigated in the proposed framework, and a critical and unique issue inherent in the proposed framework is disclosed. In particular, a lazy client can simply duplicate models shared by other clients to reap benefits without contributing its resources to FL. To address these issues, analytical and experimental results are provided to shed light on possible solutions, i.e., adding noise to achieve local differential privacy and using pseudo-noise (PN) sequences as watermarks to detect lazy clients.

Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing

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.

In recent years, to surmount the challenges of data sensitivity and data silos, federated learning (FL), a collaborative decentralized privacy preservation machine learning framework was proposed and rapidly became a current research hotspot. In this paper, we present an overview of FL workflow, as well as the communication overhead and privacy leakage problems associated with FL. In order to alleviate these two pain points, several corresponding solutions are presented in this paper along with their advantages and disadvantages. To reduce the communication overhead, this paper suggests three reliable approaches by reviewing related research including increasing the amount of computing on the edge side, model compression, and improving parallelism. To address the privacy concerns, this paper introduces three prevailing privacy preservation techniques: differential privacy, homomorphic encryption, and secure multiparty computation (SMC), and shows how to efficiently incorporate these privacy-preserving techniques into a federated learning framework by summarizing relevant literature. On the whole, this paper contributes to explaining the overall workflow of a FL framework and helps advance the understanding of FL in improving communication efficiency and enhancing privacy security.

Federated Learning(FL) has become a research hot spot in recent years because of its advantages in privacy protection. With FL, user data no longer needs to be transferred to a data center, but only trained on edge devices. Instead, the parameters of the trained model are sent to the server. FL can also use the computing power of each device to protect data from being compromised. On the other hand, in the mobile edge computing scenario, there is a large amount of data, smart phones, wearable devices and other IOT devices can not collect all the data at the beginning, the device has to waste resources and time until all the data is collected to train its local model. Furthermore, in some scenarios, the model needs to be deployed in a short period of time. Thus, incremental learning (IL) is needed, which allows continuous training with continuously collected data. In this paper, we first design a new birth-epoch computing scheme and a new batch-oriented aggregation method. Then, around these, we reformulated the Federated Incremental Learning (FIL) framework which combines FL and IL in the IoT scenario. In addition, we propose an algorithm called Latest-Compensation Federated Incremental Learning (LCFIL) that emphasizes new arrivals by adjusting the loss of each batch of data. A subtle loss function is designed to improve the convergence speed and learning performance. Extensive experiments have shown that LCFIL is effective in different data or model or data distribution Settings. Index Terms-Federated Learning, Internet of Things, Incremental Learning