Federated Device Operation Using Performance-Aware Client Training
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Solution Overview
Problem
Existing federated learning (FL) methods in distributed environments with heterogeneous hardware capabilities are slowed down by slow devices, leading to long response times, timeouts, and resource blocking, and often fail to account for varying performance across clients.
Innovation Solution
A computer-implemented method where clients provide performance indicators for their future technical performance, allowing the system to adjust training data subsets and aggregation based on these indicators, incorporating a forecasting mechanism to optimize training time and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronized federated learning is used with heterogeneous clients, then all clients must wait for the slowest client to complete training, but this causes long response times and resource blocking
Solution Approach 1:
The system transitions from static synchronized aggregation to dynamic asynchronous aggregation, where the server adaptively determines which client updates to aggregate and when, based on current system state and performance indicators, allowing the aggregation process to dynamically adjust to varying client speeds
Solution Approach 2:
Clients submit performance indicators (hardware capabilities, historical training speeds) before participating in federated learning rounds, allowing the server to pre-calculate optimized aggregation weights and strategies, anticipating which clients will be fast or slow before the actual training begins
2Manufacturing precision
If training data size is increased for all clients to improve model accuracy, then faster clients waste computational resources, but reducing data size degrades model performance
Solution Approach 1:
The system assigns different training data sizes and aggregation weights to different clients based on their local hardware capabilities and performance indicators, allowing each client to contribute appropriately sized training batches that match their computational strengths rather than forcing uniform data sizes on all clients
Solution Approach 2:
The server dynamically adjusts training parameters including batch size, number of epochs, and aggregation weights based on client performance indicators, transforming the fixed parameter approach into an adaptive parameter system that optimizes both accuracy and resource efficiency for each client
3Productivity
If the system waits for all clients to complete training before aggregation, then slow clients block the entire process, but skipping slow clients reduces their contribution to the global model
Solution Approach 1:
The system implements continuous feedback loops where clients report performance indicators and training completion status, and the server adjusts aggregation strategies in real-time based on this feedback, allowing slow clients to contribute when they complete training without blocking fast clients from proceeding
Solution Approach 2:
The federated learning process continues continuously without waiting for any single client, with the server aggregating updates as they arrive from different clients at different times, maintaining continuous model improvement rather than pausing for slow participants
Data Source
Figure 1~2e
Figure 3
AI summary
A computer-implemented method for operating a technical device through a client-server system, comprising a server (S) and clients (C1-C3), each connected to a respective technical device (TD1-TD3), wherein the respective technical device (TD1-TD3) provides a performance indicator to describe the technical performance in a predetermined future time interval, and the following steps are performed: a) Each client (C1-C3) performs training on a provided respective local model (LM1-LM3) for operating the respective technical device (TD1-TD3) with at least a subset of provided training data, wherein the size of the at least one subset of provided training data is determined by the performance indicator; b) Each client (C1-C3) provides the respective local model (LM1-LM3) to the server (S).c) Aggregating the local models (LM1-LM3) into a global model (GM) by the server and making the global model (GM) available to the clients (C1-C3), d) Receiving the global model (GM) by the respective clients (C1-C3) and updating the local model (LM1-LM3) using the previously received global model (GM), e) Operating the technical device (TD1) using the previously updated local model (LM1-LM3).