Federated Model Aggregation with Latency-Based Client Grouping
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Solution Overview
Problem
Federated learning systems face challenges in training time and model accuracy due to varying client latencies and dynamic changes in latency profiles, which can lead to inefficient model aggregation and reduced performance, especially when clients have different data distributions and latency conditions change during training.
Innovation Solution
A method that dynamically groups clients based on their latency profiles and contributions, allowing for adaptive model training and aggregation, using multiple groups to account for individual latency differences and data distribution, and updating group assignments as needed to maintain model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronous federated learning is used to aggregate knowledge between clients, then model accuracy can be maintained, but training time increases unacceptably due to clients with higher latencies of hours or days
Solution Approach 1:
The patent segments clients into different groups based on their latency characteristics (first group with lower latency, second group with higher latency). This segmentation allows the system to process faster clients separately from slower clients, preventing slow clients from bottlenecking the entire training process while maintaining their contribution to model accuracy.
Solution Approach 2:
The patent implements dynamic regrouping of clients based on their current latency profiles. Clients can transition between groups as their latency characteristics change over time, ensuring that the grouping remains adaptive and optimal throughout the training process rather than being static.
2Productivity
If slow clients are discarded in federated learning to improve training time, then training time decreases, but model accuracy degrades because discarded clients may contribute more positively to an accurate model
Solution Approach 1:
Instead of discarding slow clients, the patent segments them into a separate second group that is still processed and aggregated by the server. This allows the system to maintain contributions from all clients including slow ones, preserving model accuracy while improving overall training throughput by not waiting for the slowest clients.
Solution Approach 2:
The patent changes the aggregation parameters by implementing different handling for different client groups. The server aggregates models from the first group with lower latency and from the second group with higher latency using adjusted timing and synchronization parameters, allowing fast clients to contribute more frequently while slow clients still contribute when ready.
3Device complexity
If clients are grouped independently of their data distribution, then grouping is simpler, but performance is not optimal for non-iid data where each client may have different data
Solution Approach 1:
The patent segments clients into groups based on multiple criteria including both latency characteristics and data distribution properties. This multi-dimensional segmentation creates more meaningful groups that account for heterogeneity in non-iid data while maintaining manageable complexity through structured grouping rules.
Solution Approach 2:
The patent applies different aggregation strategies and grouping criteria to different local groups based on their specific characteristics. Each group receives tailored processing appropriate to its latency profile and data distribution, optimizing performance for each local context rather than applying a uniform approach.
4Ease of operation
If latency conditions are not monitored dynamically, then system operation is simpler, but regrouping cannot be triggered automatically when latency conditions change during training
Solution Approach 1:
The patent implements a feedback mechanism where the server continuously monitors latency conditions from clients and automatically triggers regrouping when latency profiles change. This feedback loop maintains optimal grouping without requiring manual intervention, adapting the system dynamically to changing network and computational conditions.
Solution Approach 2:
The system performs automatic self-adjustment through automated latency monitoring and dynamic regrouping. The federated learning system serves itself by automatically detecting when regrouping is needed and executing the regrouping operation without external control, maintaining optimal performance autonomously.
Data Source
AI summary
A computer-implemented method for operating a technical device via a model, wherein a) clients having a respective technical device are acquired, b) the clients are grouped into first and second groups through comparison with a respectively predetermined range of values for the respective group, c), d) a first and second group models for the first and second groups are trained and provided to a server, respectively, e) the first and the second group models are received and aggregated via the server and a global model having global model parameters is formed from the first and second group models, f) the global model is transmitted to a client and the technical device is operated via the global model, where the latency of the clients is also acquired in a) and the clients are grouped in b) based on their respective latency.