Federated Learning Model Grouping for Client Latency Imbalance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Federated learning (FL) in machine learning (ML) is hindered by latency differences among clients, leading to increased training time and potential model degradation due to temporary disconnections or non-iid data distributions, where existing methods either prolong training or discard slow clients, degrading performance.
Innovation Solution
Adaptive latency-aware FL dynamically groups clients based on their latency and contribution profiles, using model parameters to form global models, which are updated regularly to ensure accuracy and optimal training times, while considering data distribution and client weighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronous federated learning is used to aggregate knowledge between clients, then model accuracy is improved through comprehensive data aggregation, but training time increases significantly 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 clients in a structured manner, where faster clients can contribute more frequently while slower clients contribute when ready, thus reducing overall training time while maintaining model accuracy through comprehensive aggregation of all client contributions.
Solution Approach 2:
The patent implements dynamic grouping where clients are not permanently assigned to groups but are dynamically assigned based on their current latency status and contribution potential. This dynamic approach allows the system to adapt to changing network conditions and client availability, optimizing the balance between training time and model accuracy by flexibly adjusting which clients participate in which training rounds.
2Productivity
If slow clients are discarded in federated learning to improve training time, then training speed increases, but model performance degrades since discarded clients may contribute positively to accurate models
Solution Approach 1:
The patent applies local quality by treating different client groups with different strategies appropriate to their characteristics. First group clients (lower latency) are utilized more frequently and can initiate training rounds, while second group clients (higher latency) are utilized when they become available. This differentiated approach ensures that each client group contributes according to their capabilities, maintaining model performance while improving training speed by not discarding any clients.
Solution Approach 2:
The patent changes the parameter of client utilization frequency based on latency groups. Instead of uniform utilization, the system adjusts how often different client groups participate in training based on their latency characteristics and contribution potential. This parameter change allows faster clients to accelerate training while slower clients still contribute valuable information when available, balancing speed and performance.
3Ease of manufacture
If clients are grouped regardless of data distribution in federated learning, then grouping simplicity is maintained, but model accuracy reduces in non-iid data distribution where each client has different data
Solution Approach 1:
The patent performs preliminary action by assessing client contribution potential and data distribution characteristics before finalizing group assignments. The server evaluates which clients are likely to contribute positively to the model based on their data characteristics, and uses this information to form groups that optimize both simplicity and accuracy. This preliminary assessment ensures that even simple grouping strategies can achieve good model accuracy by being informed about data distribution.
4Adaptability or versatility
If latency conditions change dynamically during training, then system adaptability is required to detect when regroup should be performed, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the server continuously monitors client performance, latency patterns, and contribution quality. Based on this feedback, the server dynamically adjusts client group assignments and training round configurations. This feedback-driven approach provides the necessary adaptability to changing latency conditions while keeping system complexity manageable by using simple feedback loops rather than complex prediction algorithms.
Data Source
Figure 1~2
AI summary
A computer-implemented method for operating a technical device with a model, comprising the following steps: a) acquiring clients (C1-Cn) with a respective technical device, b) grouping (GROUP) the clients (C1-Cn) into a first group (GS) and a second group (GF) by comparison with a predetermined range of values for the respective group (GS, GF), c) training (TRAIN) a first group model for the first group (GS) and providing (UPDATE) the first group model to a server, d) training (TRAIN) a second group model for the second group (GF) and providing (UPDATE) the second group model to the server, e) receiving and aggregating (S-AGGR) the first and second group models by the server and forming a global model with global model parameters from the first and second group models.f) Transmitting the global model to a client and operating the technical device with the global model, wherein in step a) the latency of the clients (C1-Cn) is also recorded and the grouping of the clients in step b) is carried out according to their respective latency.