Federated Learning Model Splitting for Heterogeneous Client Constraints
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Solution Overview
Problem
Federated learning systems face challenges in handling system heterogeneity among clients with varying computational capabilities, leading to inefficient computational overhead and network traffic during model training.
Innovation Solution
The system employs two-dimensional model downscaling and early exits, splitting the global model along width and depth to create computationally overhead-adjusted local models, which are trained on clients based on their specific constraints, and uses self-distillation for optimization, reducing computational complexity and network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all clients are assumed to have similar computational capabilities and train the full global model, then model training can be simplified, but computational overhead increases and excludes resource-constrained clients
Solution Approach 1:
The global model is segmented into multiple local models with different complexity levels through two-dimensional downscaling (depth and width). Each client receives a local model sized according to its computational constraints, allowing resource-constrained clients to train smaller models while powerful clients can access full-capability models.
Solution Approach 2:
The system dynamically adapts model complexity to match each client's computational capabilities. Clients can select or be assigned local models of appropriate size based on their hardware resources, and the system adjusts model distribution dynamically rather than using a fixed one-size-fits-all approach.
2Use of energy by moving object
If the global model is downscaled to fit resource-constrained clients, then computational overhead is reduced, but model performance may deteriorate
Solution Approach 1:
The system changes model parameters (depth and width dimensions) to create a spectrum of local models with varying complexity. By adjusting these parameters, the system generates models that fit different computational budgets while maintaining acceptable performance through self-distillation training.
Solution Approach 2:
The self-distillation mechanism acts as an intermediary that transfers knowledge from larger global models to smaller local models. This allows smaller models to learn from the capabilities of larger models without requiring the full computational resources, bridging the performance gap between different model sizes.
3Adaptability or versatility
If multiple local models are created for different complexity levels, then adaptability to client constraints is improved, but device complexity increases
Solution Approach 1:
The framework creates a universal model family where a single global model generates multiple local models through systematic downscaling. This multi-functional approach allows the same base architecture to serve multiple client types, reducing the need for entirely separate model development for each client category.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing multiple downscaled versions of the global model before client training. This preparation work is done in advance on the server side, so clients receive ready-to-use local models without needing to perform complex model creation or selection during their training process.
4Speed
If early exits are implemented to reduce inference time, then speed is improved, but model accuracy may be compromised
Solution Approach 1:
The system implements partial action through early exit mechanisms where clients can terminate inference processing at different stages based on their constraints. Less resource-constrained clients can perform full-depth inference for higher accuracy, while resource-constrained clients can exit early with acceptable accuracy, allowing each client to perform the minimum necessary computation.
Data Source
AI summary
A computer-implemented method for training a global model on a central server in a federated learning system comprised of a plurality of nodes includes splitting the global model along a width and a depth via two-dimensional uniform downscaling of the global model. A plurality of local models are created based on the splitting of the global model. Selected ones of the plurality of local models are trained on respective selected ones of a plurality of clients based on computational constraints of each of the plurality of clients.