Federated AI Model Transfer for Resource-Efficient Device Operation
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Solution Overview
Problem
The development of basic AI models is resource-intensive and primarily controlled by large companies, limiting smaller organizations' participation and hindering diversity and innovation, while existing methods for creating these models are difficult due to computing infrastructure and dataset requirements.
Innovation Solution
A computer-implemented method using federated learning and knowledge distillation to create lightweight models from a base model, allowing decentralized training and bidirectional knowledge transfer between servers and clients, enabling efficient adaptation and customization for various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic AI models are trained using traditional centralized methods, then model accuracy and performance are improved, but resource consumption and development costs increase significantly
Solution Approach 1:
The patent segments the model training process into two distinct phases: a centralized pre-training phase that establishes foundational knowledge, and a decentralized federated learning phase that enables distributed fine-tuning. This segmentation allows the system to leverage both centralized computational power for initial model creation and distributed resources for subsequent training, reducing the resource burden on any single entity while maintaining model accuracy.
Solution Approach 2:
The patent introduces a server as an intermediary that coordinates the federated learning process. This server receives model updates from multiple clients, aggregates them, and manages the training workflow without requiring direct data exchange between clients. The intermediary enables efficient resource utilization by centralizing coordination functions while distributing computational work across multiple participants.
2Measurement precision
If basic AI models are developed by large companies with substantial resources, then model quality and performance are improved, but accessibility and participation of smaller organizations are reduced
Solution Approach 1:
The patent divides model development into phases that can be performed by different entities with different resource levels. Large organizations can contribute to the pre-training phase with their substantial resources, while smaller organizations can participate in the federated learning phase with their local data and computational capabilities. This segmentation enables multi-level participation while maintaining overall model quality.
Solution Approach 2:
The patent creates a universal federated learning framework that can accommodate participants with varying resource levels. The system is designed to accept contributions from both well-resourced and resource-constrained organizations, allowing the same model to benefit from diverse data sources and computational contributions across the organization spectrum.
3Adaptability or versatility
If complete base models are transferred to multiple clients for training, then model customization and local adaptation are improved, but data transfer time and communication overhead increase
Solution Approach 1:
The patent extracts and transfers only the essential model components and training frameworks to clients, rather than complete base models. Clients receive what is necessary to perform local fine-tuning on their specific data, reducing transfer time and communication overhead while still enabling effective local customization through the federated learning process.
4Reliability
If federated learning is implemented across multiple distributed clients, then data privacy and security are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent employs a central server as an intermediary to manage the complexities of federated learning coordination. This server handles model aggregation, training orchestration, and communication management, thereby reducing the coordination burden on individual clients while maintaining the privacy benefits of decentralized training. The intermediary absorbs much of the system complexity, making federated learning more accessible to participants.
Data Source
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Figure 3
AI summary
A computer-implemented method for operating a technical device (TD1) by a system (SYS) with a first server (S1) and several clients (C1-C3), wherein the following steps are performed: a) Provisioning a basic model (FM) to the first server (S1), b) Creation of a lightweight model (LFM) from the basic model (FM) by the first server (S1) through knowledge transfer, wherein the lightweight model (LFM) has a model structure that is a subset of the model structure of the basic model (FM), c) Transfer of the lightweight model (LFM) from the first server (S1) to the clients (C1-C3), d) Creation and training of a local model (LM1-LM3) from the lightweight model in the respective clients (C1-C3) with the respective provided training data, e) Transfer of the trained local model (LM1-LM3) from the respective clients (C1-C3) to the first server (S1), f) Aggregating the received local models (LM1-LM3) into a global model (GM),in the first server (S1), g) Training the basic model (FM) with the global model (GM) by the first server (S1) or by a connected second server (S2) of the system (SYS), which is located in a cloud, h) Transferring the global model (GM) from the first server (S1) to a respective client (C1-C3), which is connected to the technical device (TD1), and operating the technical device (TD1) with the received model (GM), i) Proceeding with step a).,