LMRPID-397450
Page 53
27th June 2024
Federated Learning for Secure and Scalable Distributed Systems in Cloud Computing Environments
Researcher- Ridanul Islam | LGMID-27199320190101881
Reviewed by:
1. Prof. Victoria Carter
2. DH Sakib
3. Taskin Karim
Paper preview
1. Abstract
2. Introduction
3. Literature Review
4. Methodology
5. Findings
6. Conclusion
7. References
Abstract
Data privacy and scalability have emerged as significant concerns in the age of cloud computing, where distributed systems are increasingly prevalent. Traditional machine learning models are typically centralized, raising issues related to data privacy, regulatory compliance, and security risks. In contrast, Federated Learning (FL) offers a decentralized approach, enabling multiple devices or nodes to collaboratively train a shared machine learning model without transferring raw data to a central server. FL enhances data privacy, mitigates risks associated with data breaches, and addresses regulatory challenges by keeping data local and sharing only model updates. This paper explores the application of Federated Learning for secure and scalable distributed systems in cloud computing environments. The goal is to develop a robust framework that addresses critical challenges, including security, privacy, scalability, and communication efficiency in distributed systems. To tackle security concerns in federated learning, the paper integrates advanced cryptographic techniques, such as homomorphic encryption and Secure Multi-Party Computation (SMPC). These methods ensure that the encrypted model updates exchanged between clients and servers prevent eavesdropping, tampering, or unauthorized access. Differential privacy further strengthens the system by ensuring that sensitive information cannot be inferred from the shared model updates. This combination of cryptographic techniques enhances data confidentiality and resilience against adversarial attacks in federated learning systems. Scalability is another critical factor considered in the proposed framework, particularly given the heterogeneity of devices and their varying computational capabilities in distributed environments. The paper discusses strategies to reduce communication overhead, optimize resource allocation, and improve model aggregation. A key approach is the Federated Averaging Algorithm (FedAvg), which allows the server to efficiently aggregate model updates from multiple devices with minimal communication overhead. This method enhances the scalability of federated learning, enabling the management of large numbers of distributed nodes without compromising performance or model accuracy. The framework will be evaluated using real-world datasets in a simulated cloud computing environment, testing its performance across various scenarios. Experimental results indicate that the proposed framework provides strong privacy protection while maintaining high model accuracy with increasing participating devices. Moreover, the system is highly scalable, keeping communication costs low and ensuring computational efficiency even in large-scale environments. Secure aggregation techniques minimize communication overhead, making this framework suitable for deployment in resource-constrained settings. In conclusion, this paper presents a comprehensive federated learning framework that effectively addresses the challenges of security, privacy, and scalability in cloud computing environments. The proposed solution enables the secure deployment of machine learning models on distributed systems without compromising data sovereignty or system performance. Future research will focus on optimizing the framework for real-world deployments, considering practical constraints such as network latency, device failures, and other operational limitations.
References
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Keywords
federated learning, cloud computing, distributed systems, data privacy, scalability, secure multi-party computation, homomorphic encryption, differential privacy, FedAvg, communication efficiency, model aggregation, cryptography, machine learning, security, privacy
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