Federated Learning Model Clustering for Skewed Data
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Solution Overview
Problem
Current federated learning systems fail to accurately account for skewed user data at client devices, leading to machine learning models that lack accuracy and precision, as they do not represent the user data across all client devices effectively.
Innovation Solution
The system generates anonymized user data profiles at local devices, clusters them centrally, trains machine learning models for each cluster, validates them across other clusters, and aggregates weights to create a combined model that maximizes prediction accuracy, ensuring the model is representative of the entire user data population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning systems train models using local user data at client devices, then user privacy is protected and data security is maintained, but the resulting models lack accuracy and precision due to skewed user data distribution across devices
Solution Approach 1:
The system segments the federated learning process into distinct phases: local data profiling at client devices, centralized cluster formation based on profile similarity, and cluster-specific model training. This segmentation allows privacy-preserving local processing while enabling centralized coordination to address data skewness, thereby improving model accuracy without compromising privacy.
Solution Approach 2:
The system introduces user data profiles as an intermediary representation between raw local user data and the federated learning model. These profiles capture essential data characteristics while preserving privacy, and serve as the basis for forming clusters of devices with similar data distributions. This intermediary enables accurate model training on representative data without exposing actual user data.
2Quantity of substance
If federated learning systems use all available local user data for training, then more data is utilized for model improvement, but skewed data distributions across devices lead to poor model generalization and reduced precision
Solution Approach 1:
The system applies local quality by creating device-specific user data profiles that capture the unique characteristics of local data distributions. Each device's profile reflects its specific data skewness patterns, enabling the system to tailor training strategies to local conditions while maintaining overall model precision through centralized cluster coordination.
Solution Approach 2:
The system changes the parameter representation from raw user data to aggregated user data profiles, and further to cluster assignments. This parameter transformation reduces the impact of skewed data distributions by grouping devices with similar profiles, thereby improving model precision while still utilizing the full volume of local training data through their respective clusters.
3Measurement precision
If centralized systems collect all user data for model training, then model accuracy can be maximized, but user privacy and data security are compromised
Solution Approach 1:
The system extracts essential characteristics from local user data to create user data profiles, which are then used for cluster formation and model training guidance. This extraction process removes personally identifiable information and sensitive data while retaining the structural patterns needed for accurate training, thereby maximizing model accuracy without compromising privacy.
Solution Approach 2:
The system creates simplified copies of user data in the form of aggregated profiles that capture data distribution characteristics without containing actual user information. These profile copies are transmitted to the centralized system for cluster formation, enabling accurate model training while the original sensitive data remains securely stored at local devices.
4Measurement precision
If federated learning systems train separate models for each device, then device-specific accuracy is improved, but system complexity and computational overhead increase significantly
Solution Approach 1:
The system merges devices with similar user data profiles into clusters, and trains one model per cluster rather than one model per device. This merging approach maintains device-specific accuracy by creating tailored models for each cluster while dramatically reducing system complexity compared to training individual models for every device. The centralized system coordinates cluster formation and model distribution.
Data Source
AI summary
Methods and systems for generating federated learning models. In some aspects, the system receives, from each client device, user data profiles that are anonymized with respect to users associated with user data stored locally at a client device. The system processes the user data profiles to generate a plurality of clusters. For each cluster, the system transmits, to one or more client devices corresponding to a cluster, a first instruction to train a machine learning model on user data corresponding to user data profiles included in the cluster and a second instruction to validate the machine learning model with respect to user data corresponding to one or more clusters of the plurality of clusters other than the cluster to generate a prediction accuracy metric. The system determines, from the plurality of clusters, a first cluster based on associated prediction accuracy metrics.