Federated Learning Feedback Loop for Heterogeneous Data Convergence
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Solution Overview
Problem
Federated learning systems face challenges in converging training data across distributed client devices due to heterogeneous data distributions, leading to slowed convergence and reduced model accuracy, as existing methods fail to effectively handle divergent local datasets while maintaining data privacy.
Innovation Solution
A method for federated learning that involves a server combining parameters from client devices, determining validity using the Central Limit Theorem, calculating parameter differences, and providing feedback to adjust local models, thereby minimizing data divergence and accelerating convergence by identifying and compensating outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is performed in a distributed manner without centralizing private data, then data privacy is maintained, but convergence time increases due to heterogeneous data distributions across clients
Solution Approach 1:
The patent implements a feedback mechanism where the server sends feedback information to client devices based on the aggregated parameters received. This feedback guides clients to adjust their local training processes and parameter updates, enabling convergence despite heterogeneous data distributions. The feedback loop allows the system to maintain privacy while reducing convergence time by directing training efforts more effectively.
Solution Approach 2:
The patent changes the parameters being exchanged in federated learning from raw training data to aggregated parameter sets (e.g., model weights, gradients). By transforming the data representation and using statistical aggregation methods, the system maintains privacy protection while improving convergence efficiency. The parameter transformation allows better handling of data heterogeneity across clients.
2Measurement precision
If training data is centralized to improve model accuracy, then model accuracy improves, but data privacy is compromised
Solution Approach 1:
The patent introduces aggregated parameters as an intermediary between client data and the global model. Instead of directly accessing or centralizing client data, the server receives aggregated parameter sets from clients, processes them, and uses them to update the global model. This intermediary mechanism enables accurate model training while preserving data privacy, as the server never directly accesses raw client data.
3Productivity
If feedback is provided to all client devices in federated learning, then model convergence improves, but communication overhead and system complexity increase
Solution Approach 1:
The patent implements a universal feedback mechanism that serves multiple functions simultaneously: it provides convergence guidance to clients, validates received parameters, monitors data distribution heterogeneity, and adjusts training strategies. This multi-functional feedback approach improves convergence speed while managing system complexity by consolidating multiple control functions into a single feedback channel.
Data Source
AI summary
The present technology discloses a federated learning network including a server and multiple client devices. The server receives a set of parameters of a local machine-learning model from each client device in a subset of the multiple client devices. The set of parameters are combined from each of the client devices in the subset to generate an integrated set of parameters. The server then calculates a parameter difference between the integrated set of parameters and the set of parameters for each client device in the subset. Feedback is sent by the server to each client device in the subset. The feedback is applied during backpropagation of the client. If the local parameters of a client are determined to be invalid for a number of times, the client will be set as an outlier.