Federated Imputation Using Complementarity-Adjusted Averaging
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Solution Overview
Problem
Federated learning systems struggle with handling missing values in distributed data, as existing methods assume complete data and lack effective strategies for imputing missing values, particularly in complex scenarios with heterogeneous and non-identically distributed data.
Innovation Solution
A method for complementarity-adjusted federated averaging imputation that involves determining pairwise complementarity scores between local data models, aggregating and weighting them to create individualized federated averaging models for each client, and iteratively updating local imputation models to impute missing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If federated learning systems use traditional imputation methods assuming complete data, then the system architecture remains simple, but the imputation quality deteriorates significantly in complex scenarios with heterogeneous and non-identically distributed data
Solution Approach 1:
The patent implements local quality by creating individualized federated averaging imputation models for each client based on their specific data characteristics. Each client receives a customized imputation model trained on their local data distribution, allowing the system to adapt to heterogeneous and non-identically distributed data across different clients, thereby improving imputation quality without requiring a single complex centralized model
Solution Approach 2:
The patent segments the imputation process into multiple components: (1) local imputation models trained independently at each client, (2) federated averaging aggregation at the server, and (3) complementarity-adjusted weighting mechanisms. This segmentation allows each component to be optimized separately while maintaining overall system efficiency, resolving the contradiction between imputation quality and system complexity
2Measurement precision
If the system aggregates all local imputation models equally, then the aggregation process remains simple, but the imputation quality deteriorates due to ignoring data complementarity across clients
Solution Approach 1:
The patent changes the aggregation parameters by introducing complementarity-adjusted weighting coefficients that dynamically adjust the contribution of each local model based on data complementarity metrics. This allows the system to prioritize models with complementary data patterns, improving imputation quality while maintaining a relatively simple aggregation framework through parameter optimization rather than structural complexity
3Measurement precision
If federated learning processes missing values locally without collaboration, then data privacy is well protected, but the imputation quality deteriorates due to inability to leverage complementarity across clients
Solution Approach 1:
The patent introduces an intermediary federated averaging mechanism that enables information exchange between clients without direct data sharing. The server acts as a mediator, collecting local model updates, applying complementarity-adjusted aggregation, and distributing improved imputation models back to clients. This intermediary process allows leveraging cross-client data complementarity while maintaining data privacy and preventing information loss
Data Source
AI summary
A method and system are disclosed for federated imputation of missing data in distributed machine learning environments, particularly under complex missingness scenarios. The disclosed approach utilizes the complementarity of both observable and missing data distributions across multiple clients. Missing value patterns encoded within each local dataset are exploited to compute a Complementarity-Adjusted Federated Averaging (Cafe) of local imputation models. The resulting complementarity scores are then employed to generate personalized imputation models for individual clients, thereby enhancing imputation accuracy while preserving data privacy. The method is applicable in settings where data cannot be shared directly due to confidentiality constraints. Empirical results demonstrate that the Cafe approach achieves substantial performance improvements over centralized imputation techniques and existing state-of-the-art federated imputation baselines.