arXiv:2409.17754v2 Announce Type: replace Abstract: Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most Byzantine-robust algorithms proposed in the literature are designed for centralized scenarios. In this paper, we present a novel B
The increasing adoption of Federated Learning (FL) and its decentralized variant (DFL) necessitates robust security measures to protect against Byzantine attacks, driving innovation in this area.
Securing decentralized AI models is critical for trusting AI deployments in sensitive applications, preventing data poisoning, and ensuring the integrity of distributed machine learning systems.
The development of Byzantine-robust algorithms specifically for DFL enhances the trustworthiness and broader applicability of decentralized AI, moving beyond limitations of centralized security approaches.
- · Organizations deploying DFL
- · AI security researchers
- · Developers of privacy-preserving AI
- · Malicious actors performing data poisoning
- · Centralized FL systems inherently vulnerable to single points of failure
Increased adoption of DFL across various industries due to enhanced security guarantees.
New standards and regulatory frameworks around secure, decentralized AI model training.
The acceleration of edge AI and distributed intelligence in critical infrastructure and autonomous systems as security concerns are mitigated.
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