# Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems

arXiv: https://arxiv.org/abs/2010.08595 · HTML: https://mlsanity.com/paper/2010.08595/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Personalized Federated Learning via Feature Distribution Adaptation](https://mlsanity.com/paper/2411.00329/) (2024)
- [Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles](https://mlsanity.com/paper/2303.04340/) (2023)
- [Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation](https://mlsanity.com/paper/2411.04112/) (2024)
- [Privacy-Preserving Taxi-Demand Prediction Using Federated Learning](https://mlsanity.com/paper/2305.08107/) (2023)
- [A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL](https://mlsanity.com/paper/2401.00809/) (2024)
- [Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance](https://mlsanity.com/paper/2206.04731/) (2022)
- [Encoded Spatial Attribute in Multi-Tier Federated Learning](https://mlsanity.com/paper/2501.05934/) (2025)
- [Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments](https://mlsanity.com/paper/2507.05861/) (2025)
- [Personalized Federated Learning via Gaussian Generative Modeling](https://mlsanity.com/paper/2603.11620/) (2026)
- [A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning](https://mlsanity.com/paper/2603.19617/) (2026)
- [Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulation](https://mlsanity.com/paper/2609.32239/) (2026)

## Abstract (arXiv)

In this paper, we show how the Federated Learning (FL) framework enables learning collectively from distributed data in connected robot teams. This framework typically works with clients collecting data locally, updating neural network weights of their model, and sending updates to a server for aggregation into a global model. We explore the design space of FL by comparing two variants of this concept. The first variant follows the traditional FL approach in which a server aggregates the local models. In the second variant, that we call Flow-FL, the aggregation process is serverless thanks to the use of a gossip-based shared data structure. In both variants, we use a data-driven mechanism to synchronize the learning process in which robots contribute model updates when they collect sufficient data. We validate our approach with an agent trajectory forecasting problem in a multi-agent setting. Using a centralized implementation as a baseline, we study the effects of staggered online data collection, and variations in data flow, number of participating robots, and time delays introduced by the decentralization of the framework in a multi-robot setting.