Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects

As federated learning (FL) keeps dominating the way in privacy-preserving machine learning (ML), notably in finance, its long-term sustainability is confronted with the approaching challenge of quantum computing. The introduction of quantum computing poses a challenge to traditional cryptographic technique on which FL relies RSA, elliptic curve cryptography (ECC), and some homomorphic encryption (HE) methods applied for encrypting model updates during training rendering this chapter to explore the interplay between the threat of quantum computing and the FL architectures, with emphasis on the implications this interaction introduces to cybersecurity and privacy in finance applications. Quantum computers are theoretically capable of solving hard math problems that have been regarded as computationally intractable for quantum computers. The well-known algorithms like Shor’s and Grover’s provides serious risk to the cryptographic procedures FL pipelines depends on. The finance sector, by its nature involving sensitive data and dependence on secure, distributed ML systems, is particularly vulnerable. The chapter begins by summarizing the fundamentals of FL and current encryption methods adapted to protect FL workflows. It then shifts towards the essence of quantum computing, emphasizing on the notable algorithms that threaten FL. We also explore potential attack points through which quantum computing could attack FL, e.g., model inversion attacks, poisoning attacks facilitated by quantum capabilities, and threats to secure aggregation. Post-quantum cryptography (PQC) is also presented as a potential counterattack, followed by a discussion of its use case in FL platforms. This chapter attempts to close the gap between FL and quantum computing threats to the financial sector. It is an introduction to practitioners and researchers to revisit examining security assumptions in FL and move towards the quantum-augmented cyber threat model.

<title>Abstract</title> Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. This paper introduces a novel approach to automate brain tumor classification using deep learning techniques, particularly convolutional neural networks (CNNs). However, conventional centralized methods compromise patient privacy and data security. To address this issue, federated learning (FL), a collaborative paradigm enabling model training across multiple institutions and aggregating models at a central server while preserving the confidentiality of sensitive medical data, is proposed. Moreover, an aggregation function at the central server is modified to identify the effect of aggregation on global model training. In addition to that, Blockchain technology is also integrated with FL architecture to enhance privacy preservation and trust, to ensure the integrity and immutability of patient data. By synergistically integrating modified FL, CNNs, and Blockchain technology, the proposed approach achieves accuracy (98%) and security in brain tumor classification. Through this, it aims to advance the field of medical imaging while prioritizing patient privacy and data security (through Blockchain technology) in brain tumor diagnosis and treatment.

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.

The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.

As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats’ growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices. Furthermore, combining FL into cybersecurity structures can strengthen attack intelligence models by permitting real upgrades and adaptive learning mechanisms. Recently, ML and Deep Learning (DL) approaches have drawn the study community to advance security solutions for cyberattack defence mechanism models. Conventional ML and DL techniques that function with data kept on a federal server increase the main privacy issues for user information. This manuscript presents a Cyberattack Defence Mechanism System for Federated Learning Framework using Attention Induced Deep Convolution Neural Networks (CDMFL-AIDCNN) technique. The CDMFL-AIDCNN model presents an improved structure incorporating self-guided FL with attack intelligence to improve defence mechanisms across varied cybersecurity applications in distributed systems. Initially, the data preprocessing stage utilizes Z-score normalization to transform input data into a beneficial format. The Dung Beetle Optimization (DBO) technique is used in the feature selection process to identify the most relevant and non-redundant features. Furthermore, the fusion of convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A) models are employed to classify cyberattack defence mechanisms. Finally, the parameter tuning of the CBLG-A approach is performed by the growth optimizer (GO) approach. The CDMFL-AIDCNN technique is extensively analyzed using the CIC-IDS-2017 and UNSW-NB15 datasets. The comparison analysis of the CDMFL-AIDCNN technique portrayed a superior accuracy value of 99.07% and 98.64% under the CIC-IDS-2017 and UNSW-NB15 datasets.

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.

Federated learning (FL) enables decentralized, privacy-preserving machine learning by training models across distributed data without sharing raw patient information. However, most FL frameworks focus on unimodal data and overlook critical challenges in multimodal healthcare settings, such as privacy risks, fairness disparities, and reduced model interpretability. We present PRIFLEX (Privacy-Resilient Integration Framework for Learning Exchange), a novel FL framework designed for secure integration of structured and unstructured medical data. PRIFLEX combines 12-lead electrocardiograms (ECG) from the PhysioNet PTB-XL dataset and clinical notes from the Medical Information Mart for Intensive Care IV (MIMIC-IV), supporting early, late, and hybrid data fusion strategies. To safeguard model updates, it evaluates standalone and hybrid defenses using Differential Privacy (DP) and Secure Aggregation (SA) against gradient leakage, model inversion, and membership inference attacks. Results show that early fusion improves the area under the Receiver Operating Characteristic curve (AUROC) by up to 6.2%, while hybrid DP+SA reduces attack success rates by up to 84% and improves fairness with manageable system overhead. PRIFLEX also quantifies interpretability loss using SHapley Additive exPlanations (SHAP) and gradient-based methods, highlighting the trade-off between privacy and transparency. Overall, PRIFLEX sets a new benchmark for building secure, fair, and explainable federated learning systems in healthcare.

Federated Learning (FL) is a transformative paradigm that enables collaborative machine learning across multiple decentralized data sources without requiring direct data sharing. This approach has emerged as a critical solution for privacy-preserving machine learning, particularly in sensitive domains such as healthcare, finance, and IoT systems. FL frameworks employ advanced techniques such as secure aggregation, differential privacy, and homomorphic encryption to protect user data while facilitating model convergence. These frameworks address key challenges, including communication efficiency, data heterogeneity, and robustness against malicious actors. This paper reviews the state-of-the-art FL frameworks, exploring their architecture, privacy-preserving techniques, and application scenarios. Furthermore, it highlights recent advancements in secure federated learning protocols, multi-party computation strategies, and scalability improvements. The discussion extends to open challenges, such as ensuring fairness, optimizing resource consumption, and maintaining security guarantees in adversarial settings. By advancing the development of privacy-preserving FL frameworks, researchers and practitioners can unlock the potential of collaborative machine learning while upholding stringent data privacy standards.

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

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.

Additive manufacturing (AM) enables the production of customized and complex metal parts, providing greater flexibility than traditional mass production. However, maintaining consistent product quality for AM processes remains challenging due to process variability and heat emission fluctuations, which can cause defects such as overheating. Overheating occurs when non-uniform temperature distributes across the printed parts, leading to issues such as structural weaknesses. Predicting heat emissions accurately is essential to preventing such issues, but AM facilities, particularly small- and medium-sized enterprises (SMEs), generate limited and heterogeneous datasets due to unique configurations and privacy concerns, making traditional centralized predictive model training paradigms impractical. Federated learning (FL) offers a privacy-preserving solution, it alleviates data privacy and data scarcity concerns by enabling collaborative learning across decentralized datasets while keeping data local. However, its performance suffers under heterogeneous client data and varied computational resources. This creates a need to address data heterogeneity while maintaining both global model performance and individual client personalization. We propose FedScope-KD, a novel heterogeneity-aware FL framework designed to balance the scope of global and personalized learning. Client heterogeneity level is quantified prior to the FL training, to improve the model weight aggregation during training. This is achieved through component decomposition and knowledge distillation. The framework decomposes client data into process-invariant representations, which capture the fundamental nature of the laser powder bed fusion (LPBF) process, and process-variant representations, which reflect local, client-specific configuration settings. This decomposition ensures that global models benefit from collaborative learning on the homogeneous data representations, while personalized predictions remain tailored to client-specific heterogeneous data representations. A knowledge distillation (KD) mechanism further enhances the framework by efficiently transferring process-invariant knowledge from a global teacher model to local student models. We compare the heterogeneity index generation and calculate the Dynamic Time Warping (DTW) distance for both inter-client variant and invariant components. The variant components exhibit greater distances between clients, reflecting their inherent heterogeneity, while the invariant components are more closely aligned across clients. This pattern demonstrates the effectiveness of the decomposition process in separating heterogeneous and homogeneous information. The heterogeneity index is further used for weight aggregation during FL training. The framework’s performance is validated through a comparison with baseline FL methods such as FedAvg, as well as centralized and individual learning paradigms. Performance metrics include personalized performance and global accuracy with MSE. Through experiments on heat emission datasets in the LPBF process, we show that FedScope-KD successfully addresses the challenges of data heterogeneity and resource constraints, providing a robust solution of predictive modeling for SMEs.

&lt;abstract&gt;&lt;p&gt;Federated learning (FL) has attracted a lot of interests as a promising machine learning approach to protect user privacy and data security. It requires the clients to send model parameters to the server rather than private datasets, thus protecting privacy to a significant extent. However, there are several types of heterogeneities (data, model, objective and systems) in FL scenario, posing distinct challenges to the canonical FL algorithm (FedAvg). In this work, we propose a novel FL framework that integrates knowledge distillation and Bayesian inference to address this multi-dimensional heterogeneity problem. On the client side, we approximate the local likelihood function using a scaled multi-dimensional Gaussian probability density function (PDF). Moreover, each client is allowed to design customized model according to the requirement through knowledge distillation. On the server side, a multi-Gaussian product mechanism is employed to construct and maximize the global likelihood function, greatly enhancing the accuracy of the aggregated model in the case of data heterogeneity. Finally, we show in extensive empirical experiments on various datasets and settings that global model and local model can achieve better performance and require fewer communication rounds to converge compared with other FL techniques.&lt;/p&gt;&lt;/abstract&gt;

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.

A large number of mobile devices, smart wearable devices, and medical and health sensors continue to generate massive amounts of data, making edge devices’ data explode and making it possible to implement data-driven artificial intelligence. However, the “data silos” and other issues still exist and need to be solved. Fortunately, federated learning (FL) can deal with “data silos” in the medical field, facilitating collaborative learning across multiple institutions without sharing local data and avoiding user concerns about data privacy. However, it encounters two main challenges in the medical field. One is statistical heterogeneity, also known as non-IID (non-independent and identically distributed) data, i.e., data being non-IID between clients, which leads to model drift. The second is limited labeling because labels are hard to obtain due to the high cost and expertise requirement. Most existing federated learning algorithms only allow for supervised training settings. In this work, we proposed a novel federated learning framework, MixFedGAN, to tackle the above issues in federated networks with dynamic aggregation and knowledge distillation. A dynamic aggregation scheme was designed to reduce the impact of current low-performing clients and improve stability. Knowledge distillation was introduced into the local generator model with a new distillation regularization loss function to prevent essential parameters of the global generator model from significantly changing. In addition, we considered two scenarios under this framework: complete annotated data and limited labeled data. An experimental analysis on four heterogeneous COVID-19 infection segmentation datasets and three heterogeneous prostate MRI segmentation datasets verified the effectiveness of the proposed federated learning method.

Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.