Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems
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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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 2; 2026: 3.
12 papers.
- 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
- 2024Personalized Federated Learning via Feature Distribution Adaptationcited by 13
- 2023Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehiclescited by 4
- 2024Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigationcited by 4
- 2023Privacy-Preserving Taxi-Demand Prediction Using Federated Learningcited by 2
- 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1
- 2022Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurancecited by 0
- 2025Encoded Spatial Attribute in Multi-Tier Federated Learningcited by 0
- 2025Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environmentscited by 0
- 2026Personalized Federated Learning via Gaussian Generative Modelingcited by 0
- 2026A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learningcited by 0
- 2026Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulationcited by 0
- 2026Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulation26 Sep 2026
- 2026A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning20 Mar 2026
- 2026Personalized Federated Learning via Gaussian Generative Modeling12 Mar 2026
- 2025Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments8 Jul 2025
- 2025Encoded Spatial Attribute in Multi-Tier Federated Learning10 Jan 2025
- 2024Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation6 Nov 2024
- 2024Personalized Federated Learning via Feature Distribution Adaptation1 Nov 2024
- 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL1 Jan 2024
- 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges21 Aug 2023
- 2023Privacy-Preserving Taxi-Demand Prediction Using Federated Learning14 May 2023
- 2023Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles8 Mar 2023
- 2022Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance9 Jun 2022