TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises
arXiv:2606.04388v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity arising from non-IID distributions and decentralized security threats remain significant challenges, particularly in resource-constrained enterprise environments. This paper presents TITAN-FedAnil+, a Trust-Based Adaptive Network for blockchain-enabled federated learning in intelligent enterprises. The proposed framework introduces affinity propagation-based adaptive clustered aggregation to iden
The increasing focus on data privacy and sovereign AI necessitates robust and secure federated learning solutions, especially for resource-constrained enterprises.
This research addresses critical challenges in federated learning including data heterogeneity and decentralized security, crucial for widespread enterprise AI adoption.
The proposed framework introduces a trust-based, adaptive aggregation method improving security and efficiency for decentralized AI in enterprise settings.
- · Enterprises with sensitive data
- · Decentralized AI platform providers
- · Blockchain integrators
- · Centralized data processing models
- · Systems vulnerable to data heterogeneity challenges
Enterprises can deploy AI solutions more securely and efficiently without centralizing sensitive data.
Increased adoption of federated learning could accelerate the development of specialized, privacy-preserving AI models across various industries.
This could contribute to a more distributed and resilient global AI ecosystem, reducing reliance on centralized data monopolies.
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Read at arXiv cs.LG