2020· International Journal of Artificial Intelligence & Digital Transformation· 0 citations
TL;DR
This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability, and analyzes various FL algorithms, such as Federated Averaging (FedAvg), assessing their effectiveness in edge computing contexts.
Abstract
In the era of edge computing, where data is generated and processed at the network's edge, ensuring privacy and scalability in machine learning models is paramount. Federated Learning (FL) addresses these challenges by allowing multiple edge devices to collaboratively train models without sharing raw data. This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability. We analyze various FL algorithms, such as Federated Averaging (FedAvg) and Hybrid Federated Dual Coordinate Ascent (HyFDCA), assessing their effectiveness in edge computing contexts. Additionally, we explore techniques like inverse distance aggregation to handle non-IID data distributions and discuss the trade-offs between communication and computation in FL frameworks. Through comprehensive analysis and experimentation, this study provides insights into optimizing FL for edge computing, paving the way for more secure and scalable machine learning applications in distributed cloud environments.
With the rapid expansion of edge computing, vast volumes of sensitive data are now being generated and processed at the network's periphery, raising significant concerns about privacy and data security. Federated Analytics (FA) emerges as a transformative solution by enabling decentralized data analysis without the need to transfer raw data to central servers, thereby mitigating potential privacy breaches. This study investigates the integration of FA into edge computing ecosystems, leveraging advanced Privacy-Enhancing Technologies (PETs) such as Differential Privacy (DP), Secure Multiparty Computation (SMC), and Homomorphic Encryption (HE) to ensure robust privacy protections. A multi-layered architecture is proposed and evaluated using simulations on Raspberry Pi clusters and synthetic workload datasets to emulate real-world edge environments. Experimental results indicate that FA, especially when combined with DP, achieves a strong balance between analytical accuracy and computational efficiency, while SMC and HE offer enhanced security at the cost of increased computational overhead. The findings underscore the practicality and effectiveness of FA for privacy-preserving analytics at the edge, suggesting its potential to support compliance with data protection regulations and meet the demands of future applications. The paper concludes by emphasizing the need for further research in optimizing scalability, minimizing resource usage, and exploring synergies with emerging technologies such as 6G and intelligent orchestration platforms to fully realize the promise of federated edge analytics.
John McCarthy, M. Minsky· International Journal of Dat...· 0 citations
Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Seshagiri N· International Journal of Mod...· 0 citations
This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.
Mahabala H. N., Seshagiri N· International Journal of Mac...· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, communication constraints, and global model divergence.
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
With the rapid development of the Internet of Things (IoT) and edge computing, Federated Learning (FL) has emerged as a promising distributed framework capable of effectively leveraging distributed devices for machine learning tasks while preserving data privacy. However, in practical scenarios characterized by significant device heterogeneity and communication delays, traditional synchronous FL often suffers from inefficiency due to the straggler effect. Although Asynchronous Federated Learning (AFL) has been proposed to mitigate this, it still faces challenges regarding model staleness and training bias, which hinder convergence performance. To address these issues, this paper presents an asynchronous framework named FedQS. First, FedQS employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics. Second, to resolve the coupling between training bias and model staleness, we implement a decoupling solution via a queue scheduling algorithm. This algorithm reprocesses high-staleness model parameters on selected faster helper clients using only the helpers’ local private data while preserving the provenance of the original uploading clients, thereby aiming to reduce stale-update effects in aggregation. Finally, during the aggregation phase, the framework recalibrates aggregation weights based on client contributions to reduce training bias and improve global model accuracy. Comparative experiments on Fashion-MNIST and CIFAR-10 datasets demonstrate that FedQS outperforms four evaluated representative baselines—FedAsync, FedBuff, KAFL, and CAFL—achieving an average accuracy improvement of 8.62%.
Jia-Hui Zhou, Fang Li, Tian-Yu Shi et al.· Journal of Cloud Computing· 0 citations
This study demonstrates the efficacy of the synergy between federated learning and edge computing in IoT security contexts, providing a scalable and privacy-centric solution for anomaly detection across large-scale distributed devices.
Quan Liu, Yuanyuan Feng· Discover Artificial Intellig...· 0 citations
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