# A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups

arXiv: https://arxiv.org/abs/2312.00026 · HTML: https://mlsanity.com/paper/2312.00026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients](https://mlsanity.com/paper/2204.03304/) (2022)
- [A Survey on Blockchain Interoperability: Past, Present, and Future Trends](https://mlsanity.com/paper/2005.14282/) (2020)
- [Witnet: A Decentralized Oracle Network Protocol](https://mlsanity.com/paper/1711.09756/) (2017)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [A Hierarchical and Location-aware Consensus Protocol for IoT-Blockchain Applications](https://mlsanity.com/paper/2305.17681/) (2023)
- [Blockchain Oracle Design Patterns](https://mlsanity.com/paper/2106.09349/) (2021)
- [TopoShot: Uncovering Ethereum's Network Topology Leveraging Replacement Transactions](https://mlsanity.com/paper/2109.14794/) (2021)
- [Integrating On-chain and Off-chain Governance for Supply Chain Transparency and Integrity](https://mlsanity.com/paper/2111.08455/) (2021)

## Abstract (arXiv)

Edge computing brings a new paradigm in which the sharing of computing, storage, and bandwidth resources as close as possible to the mobile devices or sensors generating a large amount of data. A parallel trend is the rise of phones and tablets as primary computing devices for many people. The powerful sensors present on these devices combined with the fact that they are mobile, mean they have access to data of an unprecedentedly diverse and private nature. Models learned on such data hold the promise of greatly improving usability by powering more intelligent applications, but the sensitive nature of the data means there are risks and responsibilities to storing it in a centralized location. To address the data privacy required for some data in these devices we propose the use of Federated Learning (FL) so that specific data about services performed by clients do not leave the source machines. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. However, the naive use of FL in those scenarios exposes it to a risk of corruption, whether intentional or not, during the training phase. To improve the security of the FL structure, we propose a decentralized Blockchain-based FL in an edge computing scenario. We also apply blockchain to create a reward mechanism in FL to enable incentive strategy for trainers.