# Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources

arXiv: https://arxiv.org/abs/2402.04273 · HTML: https://mlsanity.com/paper/2402.04273/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [Model-Agnostic Multi-Agent Perception Framework](https://mlsanity.com/paper/2203.13168/) (2022)
- [Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication](https://mlsanity.com/paper/2212.08273/) (2022)
- [Learning to Communicate and Correct Pose Errors](https://mlsanity.com/paper/2011.05289/) (2020)
- [Among Us: Adversarially Robust Collaborative Perception by Consensus](https://mlsanity.com/paper/2303.09495/) (2023)
- [Evading DeepFake Detectors via Adversarial Statistical Consistency](https://mlsanity.com/paper/2304.11670/) (2023)
- [The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research](https://mlsanity.com/paper/2301.07325/) (2023)
- [Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2207.05195/) (2022)

## Built on by (most cited first, 5 of 5)

- [V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions](https://mlsanity.com/paper/2403.11371/) (2024)
- [CoMamba: Real-time Cooperative Perception Unlocked with State Space Models](https://mlsanity.com/paper/2409.10699/) (2024)
- [V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2503.15435/) (2025)
- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2509.09310/) (2025)

## Abstract (arXiv)

The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system. The data silos by the above Distribution Gap could result in a significant performance decline in multi-agent perception. In this paper, we thoroughly examine the impact of the distribution gap on existing multi-agent perception systems. To break the data silos, we introduce the Feature Distribution-aware Aggregation (FDA) framework for cross-domain learning to mitigate the above Distribution Gap in multi-agent perception. FDA comprises two key components: Learnable Feature Compensation Module and Distribution-aware Statistical Consistency Module, both aimed at enhancing intermediate features to minimize the distribution gap among multi-agent features. Intensive experiments on the public OPV2V and V2XSet datasets underscore FDA's effectiveness in point cloud-based 3D object detection, presenting it as an invaluable augmentation to existing multi-agent perception systems.