Federated Learning Client Removal via Cross-Performance Evaluation
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Solution Overview
Problem
Federated learning with extreme Non-IID data leads to unstable learning and performance degradation, requiring identification of problematic clients which invades data privacy, necessitating a method to remove clients without compromising privacy.
Innovation Solution
Calculating cross-performance values between clients to determine which clients to remove, training local models using local epochs and early stopping, and using the client with better performance to train a global model, ensuring data privacy is maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is performed by repeatedly and collectively averaging parameters of the model trained in each local client, then the global model can be learned using data privacy protection, but the learning becomes unstable and performance is significantly degraded when clients have extreme Non-IID data
Solution Approach 1:
The system performs preliminary evaluation of each client's data quality by training local models and calculating cross-performance values before participating in global model training. This preliminary action identifies and removes clients with extreme Non-IID data in advance, preventing them from degrading the overall learning stability and performance of the federated learning system.
2Reliability
If clients with extreme Non-IID data are removed to improve learning stability, then model performance is improved, but data privacy is invaded to identify which clients to remove
Solution Approach 1:
The system introduces an intermediary evaluation mechanism that assesses client data quality through cross-performance metrics without directly accessing or revealing private client data. The server calculates performance values by having clients train models on their own data and evaluate on other clients' data, creating an indirect assessment that identifies problematic clients while preserving their data privacy.
Solution Approach 2:
The system replaces the traditional mechanical approach of directly inspecting client data to identify problematic clients with a computational approach using cross-performance evaluation. Instead of mechanically reviewing client datasets, the system uses model performance metrics as a substitute indicator to indirectly identify clients with extreme Non-IID data, thereby maintaining privacy while achieving the same goal.
3Manufacturing precision
If local models are trained with sufficient local epochs to improve model quality, then training time increases, but early stopping may prevent adequate training
Solution Approach 1:
The system dynamically adjusts the number of local epochs based on early stopping criteria and cross-performance evaluation results. Clients that demonstrate poor cross-performance are identified as having extreme Non-IID data and are removed from the federated learning process, preventing wasted training time on clients that would not contribute positively to the global model. This dynamic approach optimizes the balance between training quality and time efficiency.
Data Source
AI summary
The present disclosure relates to a method and an apparatus for federated learning of an artificial intelligence model. According to an exemplary embodiment of the present disclosure, a federated learning method of an artificial intelligence model includes: training a first local artificial intelligence model and a second local artificial intelligence model using data sets of a first client and a second client among the plurality of clients; calculating performance values for the first local artificial intelligence model and the second local artificial intelligence model by transmitting the first local artificial intelligence model to the second client and transmitting the second local artificial intelligence model to the first client; comparing the performance values to remove one of the first client and the second client; and training a global model using a client which is not removed.