Microsoft AI Proposes OrbitalBrain: Enables Distributed Machine Learning in Space with Inter-Satellite Links and Strategies to Develop Constellation-Aware Services

Earth observation (EO) constellations capture many high-resolution images every day, but most of them do not reach the ground during model training. Downlink bandwidth is the main bottleneck. Images can remain in orbit for days while ground models train with partial and delayed data.

Microsoft researchers have introduced the ‘OrbitalBrain’ framework as an alternative. Instead of using satellites only as sensors that transmit data to Earth, it turns a nanosatellite constellation into a distributed training system. Models are trained, integrated, and updated directly in space, using space computing, inter-satellite links, and predictive power and bandwidth planning.

BentPipe Bottleneck

Most commercial pipelines use the BentPipe model. Satellites collect images, store them in space, and dump them to ground stations whenever they pass overhead.

The research team is exploring a Planet-like constellation with 207 satellites and 12 ground stations. At maximum capture rate, the system captures 363,563 images per day. At 300 MB per image and the practical constraints of downlinks, only 42,384 images could be streamed at that time, about 11.7% of what was shot. Even if the images are compressed to 100 MB, only 111,737 images, about 30.7%, reach the bottom within 24 hours.

Limited internal storage adds another hurdle. Old images must be removed to make room for new ones, which means that many potentially useful samples are not available for ground-based training.

Why Traditional Integrated Learning Is Not Enough

Federated Learning (FL) appears to be a clear fit for satellites. Each satellite can train locally and send model updates to the ground server for integration. The research team is testing several FL bases adapted to this setting:

• AsyncFL
• SynchronizeFL
• FedBuff
• FedSpace

However, these methods require more stable communications and more flexible capabilities than satellites can provide. When the research team simulates realistic orbital variables, periodic ground contact, relative forces, and non-iid data for all satellites, these basic methods show unstable convergence and a significant decrease in accuracy, in the range of 10%–40% compared to ideal conditions.

The accuracy-time curve flattens and curves, especially when the satellites are separated from the substations for a long time. Most local updates happen long before they are integrated.

OrbitalBrain: Constellation-Centric Training in Space

OrbitalBrain starts from 3 observations:

1. Constellations are typically used by a single entity, so raw data can be shared across satellites.
2. Orbits, ground station visibility, and solar energy can be predicted from orbital properties and energy models.
3. Inter-satellite links (ISLs) and space accelerators are now operating on nano-satellites.

The frame reveals 3 actions for each satellite in the editing window:

• Local Compute (LC): train the spatial model from the stored images.
• Model Integration (MA): swap and combine model parameters over ISLs.
• Data Transfer (DT): exchange raw images between satellites to reduce data skew.

A cloud-based controller, accessible via ground stations, calculates a predictive schedule for each satellite. The schedule determines which action will be prioritized in each upcoming window, based on energy forecasts, storage, orbital visibility, and connectivity opportunities.

Main Components: Profile, MA, DT, Executor

• Target performance profile
• Integration model over ISLs
• Label recalibration data transmitter
• Legacy

Test setup

OrbitalBrain is implemented in Python on top of the CosmicBeats orbital simulator and the integrated FLUTE learning framework. The onboard compute is modeled as an NVIDIA-Jetson-Orin-Nano-4GB GPU, with limited power and communication parameters from public satellite and radio specifications.

The research team simulates the 24-hour trajectories of 2 real stars:

• Planet: 207 satellites with 12 channels.
• Spire: 117 satellites.

They examine 2 EO classification functions:

• fMoW: about 360k RGB images, 62 classes, DenseNet-161 with last 5 trainable layers.
• So2Sat: about 400k multispectral images, 17 classes, ResNet-50 with last 5 trainable layers.

Results: fast time accuracy and high precision

OrbitalBrain is compared to BentPipe, AsyncFL, SyncFL, FedBuff, and FedSpace under full physical constraints.

For fMoW, after 24 hours:

• Planet: OrbitalBrain reaches 52.8% maximum accuracy of 1.
• Spire: OrbitalBrain reaches 59.2% maximum accuracy of 1.

For So2Sat:

• Planet: 47.9% top-1 accuracy.
• Spire: 47.1% top-1 accuracy.

These results improve over the best baseline by 5.5%–49.5%, depending on the data set and constellation.

In terms of time accuracy, OrbitalBrain achieves a speed of 1.52×–12.4× compared to more traditional or blended learning methods. This comes from using satellites that cannot currently reach the ground station by combining ISLs and re-balancing the data distribution with DT.

Ablation studies show that disabling MA or DT significantly reduces both convergence speed and final accuracy. Further testing shows that OrbitalBrain remains robust when cloud cover obscures part of the image, when only a small set of satellites participate, and when image sizes and resolutions vary.

AI satellite payload implications

OrbitalBrain shows that model training can take place in space and that satellite constellations can act as distributed ML systems, not just data sources. By coordinating spatial training, model integration, and data transmission under tight bandwidth, power, and storage constraints, the framework enables new models for tasks such as forest fire detection, flood monitoring, and climate analysis, without waiting days for data to reach terrestrial data centers.

Key Takeaways

1. BentPipe downlink is the main bottleneck: Constellations such as the EO planet can only reduce 11.7% of the images taken 300 MB per day, and about 30.7% even with 100 MB compression, which greatly limits the training of the ground-based model.
2. Conventional integrated learning fails under the limitations of a real satellite: AsyncFL, SyncFL, FedBuff, and FedSpace decrease by 10%–40% in accuracy when using realistic orbital dynamics, periodic coordinates, energy limits, and non-iid data, leading to unstable convergence.
3. OrbitalBrain schedules integration, integration, and data transfer in orbit: The cloud controller uses orbit predictions, power, storage, and connection possibilities to choose Local Compute, Model Aggregation with ISL, or Data Transfer for each satellite, increasing the utility of each action.
4. Label recalibration and model stiffness are handled transparently: Guided profiler tracks the intensity and loss of the model to define computational performance, while the data transfer uses the Jensen–Shannon difference in the histogram labels to drive the exchange of raw images to reduce non-iid effects.
5. OrbitalBrain delivers high accuracy and up to 12.4× faster accuracy time: In simulations on the Planet and Spire constellations with fMoW and So2Sat, OrbitalBrain improves the final accuracy by 5.5%–49.5% over the baseline BentPipe and FL and achieves a speedup of 1.52×–12.4× in time accuracy.

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