Joint Overfitting Score Sharing for Federated Learning Isolation Detection

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Solution Overview

Problem

Federated learning systems lack effective defense mechanisms for detecting client isolation attacks, which can lead to biased models and data leakage, with clients unable to verify if they are under attack.

Innovation Solution

Implement joint overfitting verification protocols among multiple clients using overfitting scores, comparing these scores with cryptographic authentication to identify and prevent client isolation attacks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If federated learning is implemented as a distributed framework for machine learning, then privacy is improved by keeping data local, but security is worsened by vulnerability to client isolation attacks

Engineering Contradiction:

Improveprivacy guaranteeVSAvoidclient isolation attack vulnerability

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary overfitting monitoring and client isolation detection before the attack can fully compromise the model. By continuously tracking overfitting metrics during the federated learning process, the system can identify suspicious clients early and take preventive actions such as excluding them from further training rounds, thereby protecting the overall system security while maintaining the privacy benefits of federated learning

Inventive Principle:

Principle #10Preliminary action

2Device complexity

If clients do not perform verification, then system complexity is reduced, but detection capability is worsened by inability to detect isolation attacks

Engineering Contradiction:

Improveverification mechanism complexityVSAvoidisolation attack detection capability

Core Design Contradiction:

Device complexityVSDifficulty of detecting and measuring

Solution Approach 1:

The system implements a feedback mechanism where clients monitor their own overfitting metrics and share anonymized monitoring data with the server. The server aggregates this feedback from multiple clients to detect patterns indicative of isolation attacks. This approach enables detection capability without requiring complex verification mechanisms at each client, as the detection emerges from aggregated feedback patterns rather than individual client complexity

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Each client independently monitors its own overfitting metrics using simple local computations on its training and validation datasets. This self-service approach allows clients to generate detection-relevant data without external intervention, reducing system complexity while enabling the collective detection capability through aggregated monitoring information from all participants

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250294034A1Joint overfitting test for client isolation detection in federated learning

Publication Date: 2025.09.18 DELL PROD LP

AI summary

A joint overfitting method for detecting client isolation attacks in a federated learning system. Clients generate overfitting scores related to training a local model with local data. Multiple clients share their overfitting scores with each other such that each client can compare their overfitting scores with the overfitting scores of other clients. This allows anomalous scores to be identified. When anomalous scores, such as scores that relate to different distributions, are identified, the client is identified to a server and a security protocol may be initiated.