# Centralized vs. Decentralized Security for Space AI Systems? A New Look

arXiv: https://arxiv.org/abs/2509.20395 · HTML: https://mlsanity.com/paper/2509.20395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [Noiseless Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2404.09536/) (2024)
- [An Efficient Privacy-aware Split Learning Framework for Satellite Communications](https://mlsanity.com/paper/2409.08538/) (2024)

## Abstract (arXiv)

This paper investigates the trade-off between centralized and decentralized security management in constellations of satellites to balance security and performance. We highlight three key AI architectures for automated security management: (a) centralized, (b) distributed and (c) federated. The centralized architecture is the best option short term, providing fast training, despite the hard challenge of the communication latency overhead across space. Decentralized architectures are better alternatives in the longer term, providing enhanced scalability and security.