Performance Analysis and Optimization for Federated Learning Applications with PySyft-based Secure Aggregation

This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to collaboratively train models without sharing their local datasets. Aggregation is a pivotal aspect of FL, as it integrates model updates from diverse clients into a unified global model while addressing critical challenges such as data heterogeneity, scalability, and privacy preservation. The study categorizes aggregation strategies into three primary approaches: data-centric, model-centric, and secure aggregation, each tailored to address distinct problems in FL systems. Data-centric strategies focus on addressing non-independent and identically distributed (non-IID) data across clients, ensuring that model updates account for imbalances in data distributions. Model-centric strategies optimize the aggregation of model parameters, emphasizing techniques such as weighted averaging and model distillation to improve model performance across clients with diverse data characteristics. Secure aggregation techniques aim to enhance privacy and robustness, protecting client data from potential adversarial threats through encryption-based methods like secure multi-party computation (SMPC) and Byzantine-resilient techniques. The analysis delves into the advantages and limitations of these aggregation strategies, particularly their role in tackling challenges like non-IID data, communication efficiency, and resistance to adversarial attacks. Furthermore, the article identifies existing research gaps in FL, including the need for more scalable and robust aggregation methods capable of reducing communication costs, enhancing privacy guarantees, and improving performance in highly heterogeneous environments. These insights provide a roadmap for future research aimed at advancing aggregation strategies in FL to improve model accuracy, security, and efficiency in real-world applications.

Federated learning (FL) has become a promising technology to provide edge Artificial Intelligence (AI) due to its advantages in privacy protection and reduced communication costs. However, FL is still confronted with privacy leakage issues because the sharing local model may expose the training data information. Existing works typically utilize secure aggregation techniques to eliminate privacy leakage, where local model parameters in FL are obfuscated before they are sent to the aggregator. Nevertheless, secure aggregation makes poisoning attacks more convenient given that existing anomaly detection methods mostly require access to plaintext local models. A Poisoning-Defense Secure Aggregation in FL (PDSA-FL) is proposed to enhance the privacy protection of honest clients and defend against poisoning attacks from malicious clients. Specifically, a Secure Aggregation scheme based on Random Parameters Decomposition (SARPD) is designed to protect client privacy during the FL aggregation process and eliminates the impact of dropped clients on the aggregation results. Secondly, a Poisoning Detection method based on Similarity Grouping (PDSG) is proposed to mitigate the impact of poisoning attacks on the global model of FL without leaking client model parameters. The security analysis discusses the effectiveness of the proposed PDSA-FL in terms of privacy protection. Extensive simulation results show that PDSA-FL can effectively defend against poisoning attacks, significantly improve the convergence performance of global models, and reduce the computation time of clients.

Federated Learning (FL) has transformed AI applications in healthcare by enabling collaborative model training across multiple institutions while preserving patient data privacy. Despite its advantages, FL remains susceptible to security vulnerabilities, including model inversion attacks, adversarial data poisoning, and communication inefficiencies, necessitating enhanced privacy-preserving mechanisms. In response, this study introduces Privacy-Preserving Federated Learning (PPFL), an advanced FL framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Homomorphic Encryption (HE) to ensure data confidentiality while maintaining computational efficiency. I rigorously evaluate PPFL using Federated Averaging (FedAvg), Secure Aggregation (SecAgg), and Differentially Private Stochastic Gradient Descent (DP-SGD) across real-world healthcare datasets. The results indicate that this approach achieves up to an 85% reduction in model inversion attack success rates, enhances privacy efficiency by 30%, and maintains accuracy retention between 95.2% and 98.3%, significantly improving security without compromising model performance. Furthermore, comparative visual analyses illustrate trade-offs between privacy and accuracy, scalability trends, and computational overhead. This study also explores scalability challenges, computational trade-offs, and real-world deployment considerations in multi-institutional hospital networks, paving the way for secure, scalable, and privacy-preserving AI adoption in healthcare environments.

Over the past few years, machine learning has been responsible for the rapid advancements in fields such as computer vision, natural language processing and speech recognition. No small part of this success is due to data becoming more and more available, often being collected in privacy-invasive ways. The aim of this work is to study the use of two privacy-preserving solutions for training machine learning models: Federated Learning (FL) and Secure Multiparty Computation (MPC). Federated learning is a subfield of machine learning that allows training models on a large, decentralized corpus of data residing on edge devices like smartphones. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. By leveraging secure multiparty computation, it can be ensured that the server cannot inspect any individual user's update. To assess the feasibility of these approaches in different settings, a client-server architecture was implemented in Python and multiple experiments were run on datasets made available by LEAF in order to investigate ways of improving the overall performance of the models trained in a federated manner.

This systematic review examines the role of federated learning (FL) as a privacy-preserving paradigm for enterprise decision systems, synthesizing evidence from 187 peer-reviewed studies. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates algorithmic, systems, security, sectoral, and governance perspectives to provide a comprehensive account of current knowledge. Findings highlight that foundational algorithms such as FedAvg, FedProx, and SCAFFOLD dominate the methodological landscape, with significant adaptations emerging to address non-IID and unbalanced datasets across distributed organizational silos. Privacy-preserving mechanisms—including differential privacy, secure aggregation, homomorphic encryption, and multiparty computation—were consistently applied as layered defenses, balancing mathematical guarantees with empirical resilience. The synthesis further revealed critical vulnerabilities to model poisoning, backdoor attacks, and gradient leakage, alongside defensive strategies such as robust aggregation, anomaly detection, and differential privacy clipping. Sector-specific implementations demonstrate FL’s practical utility in healthcare, finance, retail, logistics, telecommunications, and public services, where it enables collaborative modeling without violating data residency or confidentiality requirements. Governance and ethical frameworks, particularly GDPR, CCPA, and the NIST Privacy Framework, were found to shape deployment practices, while documentation artifacts such as datasheets, model cards, and privacy budget ledgers ensure accountability and transparency. Comparative surveys position FL as an integrative socio-technical architecture that unites distributed optimization, privacy engineering, adversarial robustness, and AI governance into a coherent enterprise-ready model. The review concludes that federated learning provides enterprises with a scalable, secure, and ethically aligned approach to leveraging distributed data while preserving trust and compliance.

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST, CIFAR-10 and CIFAR-100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Federated Learning (FL) allows several data owners to train a joint model without sharing their training data. Such a paradigm is useful for better privacy in many ubiquitous computing systems. However, FL is vulnerable to poisoning attacks, where malicious participants attempt to inject a backdoor task in the model at training time, along with the main task that the model was initially trained for. Recent works show that FL is particularly vulnerable to edge-case backdoors introduced by data points with unusual out-of-distribution features. Such attacks are among the most difficult to counter, and today's FL defense mechanisms usually fail to tackle them. In this paper, we present ARMOR, a defense mechanism that leverages adversarial learning to uncover edge-case backdoors. In contrast to most of existing FL defenses, ARMOR does not require real data samples and is compatible with secure aggregation, thus, providing better FL privacy protection. ARMOR relies on GANs (Generative Adversarial Networks) to extract data features from model updates, and uses the generated samples to test the activation of potential edge-case backdoors in the model. Our experimental evaluations with three widely used datasets and neural networks show that ARMOR can tackle edge-case backdoors with 95% resilience against attacks, and without hurting model quality.

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.

This research article is an effort to explore the intriguing fact about the Indian With the rapid advancement of artificial intelligence (AI) technology, we are seeing an explosion of data being transmitted during model training, which unfortunately raises the risk of data leakage. In an age where data privacy is paramount and regulations are becoming increasingly strict, protecting sensitive information from unauthorized access has become a pressing issue. This is where Federated Learning (FL) steps in as a promising solution, finding its way into various sectors. In this paper, we will explore the practical applications of FL in five crucial areas: healthcare, urban transportation, computer vision, the Industrial Internet of Things (IIoT), and 5G networks. We will assess how FL can be effectively implemented in these real-world scenarios to enhance privacy while ensuring model accuracy and efficiency. Additionally, we will compare the FL framework with traditional centralized methods, showcasing how FL improves data privacy and performance, as well as acknowledging some of its current limitations. We will also discuss potential future enhancements that could make FL even more effective. Lastly, we will take a look at the latest research trends and the developmental prospects within this exciting field, providing insight into how FL could shape the future of data protection and AI.

In domains such as remote healthcare, where sensitive data must be protected, Federated Learning (FL) has emerged as a radical new way to build privacy-preserving AI models. In contrast, centralized AI systems have always existed, aggregating patient data into a single repository, subject to vulnerabilities in privacy, security, and regulatory compliance. In this study, we propose a novel FL implementation in remote healthcare that supports distributed training across multiple devices and remote healthcare facilities, while preserving patient privacy. We design a hypothetical framework taking advantage of the state of the art in machine learning tools including TensorFlow Federated and privacy enhancing technologies like differential privacy and secure aggregation. We evaluate the effectiveness of FL for simulating patient vitals, symptoms, and outcomes for different healthcare institutions in the research. This shows that FL can get comparable model accuracy with centralized systems, and its privacy and scalability aspect gains much more. Communication overhead and data heterogeneity are discussed, and practical strategies around their mitigation are laid out for the practical deployment of this method. In this work, we offer a comprehensive analysis of the application of FL in the healthcare domain, particularly on how it can contribute to the security of FL-sensitive data and the development of medical AI applications. Future research direction in building privacy-preserving AI models specific to the fluctuating needs of remote healthcare environments is now feasible with these results.

Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.

Federated learning (FL) enables collaborative model training without centralizing raw data, yet practical deployments remain vulnerable to security threats such as Byzantine model poisoning, stealthy backdoor implantation, and integrity attacks that exploit the opacity of client updates. This paper presents SecureFedGuard, a security-centric FL framework that introduces a novel combination of (i) dual-view update authentication that binds each client update to a lightweight stochastic gradient fingerprint, enabling server-side integrity screening without accessing client data, and (ii) backdoor-resilient aggregation driven by cross-round spectral forensics and adaptive coordinate-wise trimming guided by an estimated benign subspace. SecureFedGuard is designed to be compatible with secure aggregation and does not require trusted hardware, public datasets for pretraining, or expensive per-client verification. We provide a simple robustness analysis that clarifies when benign updates dominate the estimated subspace under mixed benign/malicious participation. Experiments on real FL benchmarks (vision and language) under diverse threat models show that SecureFedGuard substantially improves clean accuracy and backdoor attack success rate compared with strong baselines, while adding modest communication and computation overhead. These results suggest a practical path toward integrity-preserving and backdoor-resistant FL without weakening the privacy boundary between clients and the server.

Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.