Privacy-Preserving Distributed Learning: A Comprehensive Review

Federated learning (FL) is a rapidly growing field that enables multiple parties to collaboratively train a model on their collective data without sharing the data itself. Decentralized FL takes this concept a step further by removing the central authority, allowing nodes to communicate directly with each other and share models updates. This approach has several benefits, including improved privacy and scalability, but also presents unique challenges, such as maintaining accuracy and dealing with malicious actors.
In this article, we explore the current state of decentralized FL, discussing various techniques for enhancing privacy, scalability, and security. We examine different network structures, including Poisson and power law distributions, and analyze how they impact the performance of decentralized FL protocols. We also discuss cryptographic techniques, such as differential privacy and secure aggregation, that can be used to further enhance privacy and security in decentralized FL.
Decentralized FL offers several advantages over traditional centralized FL, including improved privacy, scalability, and resilience against malicious attacks. By removing the central authority, decentralized FL makes it more difficult for attackers to manipulate the model updates or steal sensitive information. Additionally, decentralized FL can enable faster and more efficient training by reducing communication overhead between nodes.
However, decentralized FL also presents several challenges that must be addressed. One of the main challenges is maintaining accuracy in the absence of a central authority to enforce model consistency. To address this challenge, researchers have proposed various techniques, such as using differential privacy methods or incorporating domain knowledge into the model.
Another challenge in decentralized FL is dealing with malicious actors, who can manipulate the model updates to their advantage or compromise the privacy of other nodes. To mitigate these risks, researchers have proposed various security measures, such as secure aggregation protocols and cryptographic techniques for ensuring data privacy.
In conclusion, decentralized FL is a promising approach that offers several benefits over traditional centralized FL, including improved privacy, scalability, and resilience against malicious attacks. However, it also presents unique challenges that must be addressed through careful design and implementation of protocols and algorithms. By leveraging techniques such as differential privacy and secure aggregation, we can further enhance the security and privacy of decentralized FL and unlock its full potential for a wide range of applications.