Federated Learning Mission Control for Deep-Space Systems
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Solution Overview
Problem
Current mission control systems for deep-space exploration face challenges such as communication latency, limited computing resources on deep-space spacecraft, and the need for real-time subject matter expertise to resolve issues during missions.
Innovation Solution
A distributed deep-space cognitive, learning-based mission analysis and control (MAC) system that uses a federated learning configuration to perform computationally expensive tasks on near-earth or earth-based systems, updating AI models for deep-space systems, and incorporating subject matter expert inputs to enhance self-learning functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computationally expensive tasks are performed on deep-space spacecraft, then real-time data analysis capability is improved, but available computing resources are insufficient
Solution Approach 1:
A federated learning system acts as an intermediary between deep-space spacecraft and earth-based computational resources. The system distributes AI model training across multiple earth-based nodes while allowing the spacecraft to perform inference with limited local computing resources, effectively bridging the resource gap through coordinated distributed computation.
Solution Approach 2:
The computational workload is segmented into model training phases (performed on earth-based systems with abundant resources) and model inference phases (performed on the spacecraft with limited resources). This segmentation allows each system to operate within its resource constraints while achieving overall high-performance data analysis.
2Measurement precision
If more AI models are trained on earth-based systems, then model accuracy is improved, but communication latency increases when transmitting updates to deep-space systems
Solution Approach 1:
AI models are trained and updated on earth-based systems in advance, storing multiple versions of models locally. When the spacecraft establishes communication with earth, the pre-trained models are quickly transmitted without requiring real-time computation during the communication window, thus reducing the impact of communication latency.
Solution Approach 2:
The system dynamically adjusts the frequency and timing of model updates based on communication availability. During periods of communication latency, the spacecraft operates with previously updated models, and model synchronization occurs dynamically when communication channels are available, optimizing the balance between model accuracy and communication constraints.
3Reliability
If subject matter experts are continuously consulted for troubleshooting, then decision accuracy is improved, but response time increases due to communication delays
Solution Approach 1:
The spacecraft is equipped with AI models that enable autonomous troubleshooting and decision-making capabilities. Subject matter experts provide input during model training phases, but once deployed, the models independently analyze telemetry data and resolve issues without requiring continuous expert consultation, thus maintaining high decision accuracy while reducing response time.
Solution Approach 2:
The system implements a feedback mechanism where subject matter expert decisions are fed back into the AI model training process. This allows the models to learn from expert judgments and improve their autonomous decision-making capabilities over time, gradually reducing the need for real-time expert intervention while maintaining or improving decision accuracy.
4Productivity
If data engineering and analytics expertise is increased, then system performance is improved, but shortages in expertise persist
Solution Approach 1:
The system replaces the need for continuous human expert intervention with automated AI models that perform data engineering and analytics functions. The models are trained using expertise from subject matter experts during initial development, but once trained, they autonomously handle complex data analysis tasks, effectively substituting mechanical automation for human expertise in operational phases.
Data Source
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AI summary
Embodiments of the disclosure provide a deep-mission system that includes a processor system operable to perform processor system operations that include executing a first instance of a cognitive algorithm; generating data of the deep-mission system that can be used to fine-tune the first instance of the cognitive algorithm; and transmitting the data of the deep-mission system over a deep-mission communication path to a remote station (850). The remote station (850) is operable to, based at least in part on the data of the deep-mission system, perform fine-tuning operations on a second instance of the cognitive algorithm located at the remote station (850); generate updates to the cognitive algorithm based on the fine-tuning operations; and transmit the updates to the cognitive algorithm over the deep-mission communication path to the deep-mission system. The processor system operations further include using the updates to the cognitive algorithm to update the first instance of the cognitive algorithm.