One-Shot Clustering Using Data Similarity for Multi-Task Hierarchical Federated Learning

Concetti Chiave

This research paper proposes a novel one-shot clustering algorithm for Multi-Task Hierarchical Federated Learning (MT-HFL) that leverages data similarity among users to improve accuracy and efficiency while preserving data privacy.

Sintesi

• Bibliographic Information: Ali, A., & Arafa, A. (2024). Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning. arXiv:2410.02733v1 [cs.LG].
• Research Objective: This paper addresses the challenge of cluster identity estimation in MT-HFL, aiming to group users with similar tasks for efficient collaborative learning while maintaining data privacy.
• Methodology: The authors propose a one-shot clustering algorithm based on a modified data valuation method. Users calculate the eigen decomposition of their data's Gram matrix and share eigenvector matrices. They then estimate data relevance based on projected eigenvalues and use Hierarchical Agglomerative Clustering (HAC) to group users. This is followed by standard federated averaging training within each cluster and sharing of common layer weights with the global server.
• Key Findings: The proposed algorithm outperforms random clustering in terms of accuracy and variance reduction on CIFAR-10 and Fashion MNIST datasets. It effectively clusters users even with unbalanced task label distributions and data from different datasets. The algorithm also demonstrates communication efficiency by requiring only a small subset of eigenvectors for accurate clustering.
• Main Conclusions: Leveraging data similarity through the proposed one-shot clustering algorithm significantly enhances MT-HFL performance. It enables accurate user grouping, improves learning accuracy, and reduces communication costs while preserving data privacy.
• Significance: This research contributes a practical and efficient solution to the crucial challenge of user clustering in MT-HFL, paving the way for more effective and scalable federated learning applications.
• Limitations and Future Research: Future research could explore the algorithm's robustness to noisy eigenvector exchanges and investigate additional privacy-preserving mechanisms without compromising clustering accuracy.

Statistiche

Users with similar tasks have most of their training samples drawn from that particular task.
10% of labels from other tasks were assigned to each user to introduce label diversity and test robustness.
The two LPSs shared the weights of the two convolution layers with the GPS (common layers).
Five users had the majority of Task 1 labels, three users had the majority of Task 2 labels, and two users had Task 3 labels in the Fashion MNIST experiment.
Only five eigenvectors were needed to effectively cluster users with different tasks in the Fashion MNIST experiment.

Citazioni

"The essence of this work is to turn the feature heterogeneity among users from a challenge into an opportunity."
"Our main objective is to efficiently cluster users among LPSs while preserving their data privacy and minimizing communication costs, and to do so independently of the model or the class of loss function at the users."

Approfondimenti chiave tratti da

Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning

by Abdulmoneam ... alle arxiv.org 10-04-2024

https://arxiv.org/pdf/2410.02733.pdf

Domande più approfondite

How could this data similarity-based clustering approach be adapted to other types of federated learning beyond MT-HFL?

This data similarity-based clustering approach, relying on data valuation and features rather than model parameters, holds considerable potential for adaptation to other federated learning scenarios beyond MT-HFL. Here's how:

Personalized Federated Learning (PFL): In PFL, the goal is to learn personalized models for users with potentially diverse data distributions. This clustering approach can group users with similar data distributions, enabling the training of more effective personalized models. Users within a cluster could share a common base model, fine-tuned with their local data, enhancing personalization while benefiting from collaborative learning.

Federated Learning with Non-IID Data: A major challenge in FL is the presence of non-IID data, where data across users is not independently and identically distributed. This method can be used to cluster users with similar data distributions, mitigating the effects of non-IID data and improving the performance of the global model.

Robust Federated Learning: This approach can enhance robustness against malicious users or Byzantine attacks. By clustering users with similar data, outliers or malicious users exhibiting significantly different data patterns can be identified and potentially isolated, preventing them from adversely affecting the global model.

Dynamic Federated Learning: In scenarios with evolving data distributions, this clustering method can be implemented dynamically. Periodic re-clustering based on updated data relevance can ensure that users remain grouped with others having similar data, adapting to the dynamic nature of the data.

Key Considerations for Adaptation:

Privacy-Preserving Mechanisms: Adaptations should incorporate robust privacy-preserving techniques, such as differential privacy or secure aggregation, to ensure data confidentiality during the eigenvector exchange and clustering process.
Scalability: For large-scale federated learning settings with a massive number of users, efficient and scalable clustering algorithms should be explored to handle the computational complexity.
Communication Efficiency: The communication overhead associated with exchanging eigenvectors should be minimized. Techniques like quantization or sparsification of eigenvectors can be investigated.

Could the reliance on eigen decomposition pose computational challenges for users with limited resources, and how might these be addressed?

Yes, the reliance on eigen decomposition, particularly for users with limited computational resources, can pose challenges. Calculating eigen decomposition, especially for large matrices (high-dimensional data), can be computationally intensive. Here are some potential solutions:

Dimensionality Reduction: Employing dimensionality reduction techniques like Principal Component Analysis (PCA) or random projections before eigen decomposition can significantly reduce the size of the data matrix, making the computation more manageable for resource-constrained devices.

Approximate Eigen Decomposition: Instead of computing the exact eigen decomposition, approximate methods like the power iteration method or Lanczos algorithm can be used. These methods trade off some accuracy for computational efficiency, making them suitable for devices with limited resources.

Federated Eigen Decomposition: The computation of eigen decomposition can be distributed among users. Techniques like federated PCA allow for the computation of principal components (and hence, eigenvectors) in a distributed manner, reducing the computational burden on individual users.

Feature Subset Selection: Instead of using all features for eigen decomposition, a carefully selected subset of highly informative features can be used. This reduces the dimensionality of the data matrix and the computational cost of eigen decomposition.

Offloading Computation: For extremely resource-constrained devices, the computation of eigen decomposition can be offloaded to more powerful edge servers or the cloud. This requires secure communication channels to protect user data privacy.

What are the broader ethical implications of using data similarity for clustering in federated learning, particularly concerning potential biases in the data?

While data similarity-based clustering offers advantages for federated learning, it also raises ethical concerns, particularly regarding potential biases in the data:

Amplification of Existing Biases: If the training data contains biases, clustering based on data similarity can exacerbate these biases. For instance, if a dataset used for medical diagnosis is skewed towards a particular demographic, clustering based on similarity might result in models that are less accurate or even discriminatory towards under-represented groups.

Creation of Unfair or Discriminatory Clusters: Clustering solely on data similarity might inadvertently create clusters that reflect existing societal biases, even if these biases are not explicitly present in the data labels. This can lead to unfair treatment or discrimination against certain groups.

Lack of Transparency and Explainability: The clustering process based on data similarity can be complex and opaque, making it difficult to understand why certain users are grouped together. This lack of transparency can hinder the identification and mitigation of potential biases.

Privacy Concerns: While the proposed method aims to preserve privacy, the exchange of eigenvectors, even if anonymized, might still leak sensitive information about the underlying data, potentially leading to privacy violations.

Mitigating Ethical Concerns:

Bias Detection and Mitigation: Implement bias detection and mitigation techniques during both the data pre-processing stage and the clustering process. This involves analyzing the data for potential biases and employing fairness-aware clustering algorithms.

Diverse Data Collection: Strive for diverse and representative data collection to minimize the risk of amplifying existing biases. This might involve actively seeking data from under-represented groups.

Transparency and Explainability: Develop more transparent and explainable clustering methods that provide insights into the clustering decisions. This allows for better understanding and scrutiny of potential biases.

Privacy-Enhancing Technologies: Strengthen privacy-preserving mechanisms, such as differential privacy or homomorphic encryption, to further protect user data during the clustering process.

Ethical Frameworks and Guidelines: Establish clear ethical frameworks and guidelines for the use of data similarity-based clustering in federated learning. These frameworks should address issues of fairness, transparency, and accountability.