Federated Ensemble Learning with Incremental Model Updates
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Solution Overview
Problem
Existing ensemble learning techniques face challenges in determining the optimal combination of models from decentralized datasets, inefficiencies in using all client models, and difficulties in updating the ensemble when clients or models are added or removed, especially in federated learning settings where data is not shared.
Innovation Solution
A method and system for federated ensemble learning that distributes prediction models to clients, evaluates them on local datasets, and generates an ensemble model using weights based on a user-defined objective, gradient, and Hessian matrix, allowing for incremental and decremental updates without sharing raw data, and optimizing weights to minimize error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all client models are used in the ensemble, then the ensemble model achieves higher accuracy, but the computational complexity and data exchange requirements increase significantly
Solution Approach 1:
The patent extracts and identifies only the most relevant client models for the ensemble based on their performance metrics and compatibility with the federated learning objective. Instead of using all client models, the system selectively includes models that contribute most to accuracy while filtering out redundant or conflicting models, thereby reducing computational complexity while maintaining ensemble performance.
Solution Approach 2:
The patent applies partial action by using a subset of client models rather than all available models. The system determines an optimal number of models to include in the ensemble based on diminishing returns analysis, where adding beyond a certain number of models provides minimal accuracy improvement but significantly increases computational burden. This partial approach balances accuracy and complexity effectively.
2Adaptability or versatility
If models are frequently updated in the ensemble, then the ensemble adapts to changing data distributions, but the computational overhead and communication costs increase
Solution Approach 1:
The patent implements periodic updates rather than continuous updates of the ensemble model. The system determines update intervals based on data distribution drift detection and performance degradation thresholds. Updates are triggered only when necessary, such as when statistical tests detect significant data distribution changes or when validation accuracy drops below a threshold, thereby reducing computational overhead while maintaining adaptability.
Solution Approach 2:
The patent employs feedback mechanisms to monitor ensemble performance and data distribution changes in real-time. The system uses validation sets and statistical tests to detect when updates are needed, creating a closed-loop control system that triggers updates only when performance degradation or distribution shift is detected. This feedback-driven approach optimizes the balance between adaptability and computational cost.
3Reliability
If decentralized gradient descent is used for federated learning, then client privacy is preserved, but the method fails when clients are connected intermittently or use different model architectures
Solution Approach 1:
The patent segments the federated learning process into independent model evaluation and ensemble construction phases. Each client independently evaluates its local model on its own data without requiring continuous communication or synchronized updates. The ensemble is then constructed by combining these independently evaluated models, allowing clients to participate intermittently and use different model architectures while maintaining privacy through decentralized evaluation.
4Ease of operation
If existing ensemble learning techniques are applied to federated settings, then model combination is achieved, but it becomes difficult to determine the optimal combination method and efficiently select the best subset of models
Solution Approach 1:
The patent transforms the model combination problem into a parameter optimization problem by representing ensemble combinations as weight parameters. The system optimizes these parameters using federated objective functions that aggregate local performance metrics from all clients. This parameter-based approach converts the difficult combinatorial optimization problem into a more tractable parameter tuning problem that can be solved using standard optimization techniques in a federated setting.
Data Source
AI summary
A computer implemented method includes distributing a plurality of prediction models, where each of a plurality of clients initially includes at least one associated prediction model from the plurality of prediction models, among all of the plurality of clients to provide each of the plurality of clients with each of the plurality of prediction models. The plurality of prediction models is evaluated on at least a portion of a local dataset resident on each of the plurality of clients to output a quantification indicating how each of the prediction models fit at least the portion of the local dataset of each of the plurality of clients. An ensemble model is generated by applying weights to each of the plurality of prediction models based on a value, a gradient, and a Hessian matrix of a user-defined objective.