18045527. REVEALING BYZANTINE ATTACKERS THROUGH SMART RE-CLUSTERING IN FEDERATED LEARNING simplified abstract (Dell Products L.P.)

REVEALING BYZANTINE ATTACKERS THROUGH SMART RE-CLUSTERING IN FEDERATED LEARNING

Organization Name

Dell Products L.P.

Inventor(s)

Paulo Abelha Ferreira of Rio de Janeiro (BR)

Pablo Nascimento Da Silva of Niterói (BR)

Maira Beatriz Hernandez Moran of Rio de Janeiro (BR)

REVEALING BYZANTINE ATTACKERS THROUGH SMART RE-CLUSTERING IN FEDERATED LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18045527 titled 'REVEALING BYZANTINE ATTACKERS THROUGH SMART RE-CLUSTERING IN FEDERATED LEARNING

Simplified Explanation

The patent application discloses a method for identifying malicious clients in federated learning while enhancing privacy. The clients are clustered, and updates are generated in the federated learning process. When a suspect cluster is identified, clients in the suspect clusters are labeled as suspect, and clients in non-suspect clusters are labeled as fair. The clients are then reclustered and relabeled without changing the labels of previously deemed fair clients. After one or more iterations, the malicious clients are identified, and corrective actions can be taken.

• Clustering clients in federated learning to identify malicious behavior
• Labeling suspect and fair clients based on cluster analysis
• Reclustering and relabeling clients to identify malicious clients
• Enhancing privacy in the federated learning process

Potential Applications

The technology can be applied in various fields such as cybersecurity, data privacy, and machine learning.

Problems Solved

This technology addresses the issue of identifying malicious clients in federated learning while maintaining privacy and data security.

Benefits

The benefits of this technology include improved security, enhanced privacy, and more efficient detection of malicious behavior in federated learning systems.

Potential Commercial Applications

Potential commercial applications of this technology include cybersecurity software, data privacy tools, and machine learning platforms.

Possible Prior Art

One possible prior art could be the use of clustering algorithms in machine learning for data analysis and pattern recognition.

How does this technology impact the efficiency of federated learning systems?

The efficiency of federated learning systems can be improved by identifying and removing malicious clients, thus enhancing the overall performance of the system.

What are the potential limitations of this technology in real-world applications?

One potential limitation could be the computational resources required for clustering and relabeling a large number of clients in federated learning systems.

Original Abstract Submitted

Identifying malicious clients in federated learning is disclosed while enhancing privacy. The clients are clustered such that cluster updates in the federated learning are generated. When a suspect cluster is identified, clients in the suspect clusters are labeled as suspect and clients in clusters that are not suspect are labeled as fair. The clients are reclustered and the clusters and clients are relabeled without changing the labels of clients that were previously deemed fair. After one or more iterations, the malicious clients are identified, and corrective actions can be performed.