Federated Learning Server Abnormal Client Removal
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Solution Overview
Problem
Federated learning systems face challenges in detecting and managing out-of-distribution (OOD) data, as servers cannot access client training data, leading to potential model deterioration and data poisoning, which affects global model performance and reliability.
Innovation Solution
A method is proposed where a server receives weight values from clients, generates client models, validates them using a validation dataset, and removes abnormal models by calculating similarity between client vectors, updating the global model while excluding OOD models, thereby maintaining model reliability and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to protect sensitive information by training local models on client devices, then user privacy and data security are improved, but the system becomes vulnerable to OOD data and abnormal clients that can poison the global model
Solution Approach 1:
The patent introduces an intermediary validation mechanism where the server generates validation datasets and client vectors to mediate between client training data and global model aggregation. This intermediary layer enables the server to detect OOD data and abnormal clients without directly accessing sensitive client training data, thus resolving the contradiction between maintaining data security and preventing data poisoning.
Solution Approach 2:
The patent implements a feedback mechanism where client models are validated against validation datasets, generating client vectors that are compared to detect outliers. The server provides feedback by rejecting or accepting client updates based on this validation, creating a closed-loop system that continuously monitors and prevents OOD data from corrupting the global model while preserving the federated learning architecture.
2Reliability
If the server validates each client model using validation datasets and similarity checks, then the global model reliability is improved, but the computational overhead and communication costs increase
Solution Approach 1:
The patent extracts only the essential validation information (client vectors derived from validation dataset predictions) from the complete client training process. Instead of validating entire models or transmitting full training data, the system extracts compact vector representations that capture the essential characteristics needed for outlier detection, thereby reducing validation complexity while maintaining reliability.
Solution Approach 2:
The patent transforms the validation problem from model-level comparison to parameter-level analysis by using client vectors (parameter representations) for similarity assessment. This parameter transformation simplifies the validation process by converting complex model validation into straightforward vector distance calculations, reducing computational overhead while preserving detection accuracy.
3Productivity
If all client models are aggregated without validation, then the federated learning process is simple and fast, but OOD models can deteriorate the global model performance
Solution Approach 1:
The patent applies preliminary action by performing validation checks on client models before they are aggregated into the global model. The server generates validation datasets, computes client vectors, and identifies abnormal clients in advance of the aggregation step. This preliminary validation prevents OOD models from entering the aggregation process, ensuring global model performance without requiring complex post-aggregation correction mechanisms.
Data Source
AI summary
Provided is a method of removing, by a server, an abnormal client in federated learning. A method of removing, by a server, an abnormal client in federated learning may include receiving, from a user equipment (UE), first weight values trained in a first local model, generating a first client model based on the first weight values, validating the first client model by using a validation data set in order to determine whether the first client model is legitimate, and removing the first weight values based on the first client model not being legitimate.