DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
arXiv:2606.04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server. Although the power of collaboration has been verified by many state-of-the-art studies, it entails extensive gradient information exchanging among the agents and thus induces high risk of privacy leakage for the individual agents. Moreover, in real-world applications, the training data are usually non-identically and independently distributed across the agents, inducing more challenges to enable pri
The increasing focus on decentralized AI and data privacy, coupled with the computational demands of large models, makes preserving privacy in collaborative learning a timely and critical challenge.
This research addresses fundamental challenges in AI ethics and security, directly impacting the deployability and public trust in collaborative AI systems, especially in sensitive domains.
New methods for secure and private decentralized AI will enable broader adoption of collaborative machine learning without compromising individual data privacy.
- · AI ethicists
- · Privacy-focused tech companies
- · Decentralized AI platforms
- · Individuals with sensitive data
- · Malicious actors exploiting data leakage
- · Centralized AI monopolies
- · Legacy data privacy approaches
Wider adoption of decentralized AI paradigms, especially in sectors with strict privacy regulations.
Increased trust in AI applications, leading to more data sharing for societal benefits under privacy-preserving conditions.
Potential for new business models centered around secure and privacy-enhanced AI services that challenge centralized data ownership.
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Read at arXiv cs.LG