Federated Learning Model Training via Server-Side Knowledge Distillation
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Solution Overview
Problem
Current federated learning techniques face challenges in training high-quality foundation models due to low accuracy and high computational costs, especially in cross-device scenarios where resource-constrained devices are involved, and they often compromise privacy by requiring data sharing.
Innovation Solution
The method involves training a global student model on a server using local student models transmitted from client devices, where the models are trained layer-by-layer with knowledge distillation, without sharing client data, and weights are aggregated using federated averaging, offloading computational load from client devices to the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train foundation models on resource-constrained devices, then data privacy is preserved, but model training accuracy deteriorates and computational feasibility is compromised
Solution Approach 1:
A server acts as an intermediary between resource-constrained client devices and the foundation model training process. The server hosts the complete foundation model and performs computations that client devices cannot handle, while client devices contribute local data and compute resources for fine-tuning. This intermediary architecture enables accurate model training without requiring clients to share raw data, thus preserving privacy while maintaining accuracy.
Solution Approach 2:
The training process is segmented into distinct phases: (1) initial foundation model training on the server using public data, (2) distribution of the foundation model to client devices, (3) local fine-tuning on client devices using private data, and (4) aggregation of fine-tuned models back on the server. This segmentation allows each participant to operate within their computational constraints while contributing to the overall training objective.
2Manufacturing precision
If foundation models are trained in centralized systems, then model training accuracy is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The training workload is segmented between centralized server and distributed client devices. The server performs the computationally intensive initial foundation model training using public datasets, while client devices perform lighter fine-tuning operations on their private data. This segmentation reduces the computational burden on any single system while maintaining high model accuracy through the collaborative training process.
3Reliability
If cross-device federated learning is implemented on resource-constrained devices, then data privacy is maintained, but training feasibility deteriorates due to limited computational resources
Solution Approach 1:
The server serves as a capability-enhancing intermediary that compensates for the limited computational resources of resource-constrained client devices. It provides the foundation model, receives fine-tuned models, and performs aggregation operations that would be infeasible on client devices alone, thereby making training feasible while maintaining the decentralized privacy-preserving architecture.
Solution Approach 2:
The foundation model is pre-trained on the server using public datasets before being distributed to client devices. This preliminary action prepares a robust base model that requires minimal fine-tuning, reducing the computational burden on resource-constrained devices and making the training process feasible for devices with limited resources.
Data Source
AI summary
A system and a computer-implemented method of training a global student model is disclosed. The global student model and a teacher model are stored on a server and each include a first layer. The method includes transmitting local student models based on the global student model, the local student models each including an embedding layer and a first layer. The method includes receiving an embedding layer output of one of the local student models. The method includes performing a forward pass on the first layer of the teacher model, with the embedding layer output as an input, to generate a teacher model first layer output. The method includes transmitting the teacher model first layer output. The method includes receiving first layer weights of the local student models. The method includes calculating first layer weights of the global student model using the received first layer weights of the local student models.