PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning (2506.15923v1)

Abstract: Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. However, many existing approaches fail to account for the intricate gradient correlations between remote clients, a limitation that becomes especially problematic in data heterogeneity scenarios. In this work, we propose a novel FL framework utilizing Power-Norm Cosine Similarity (PNCS) to improve client selection for model aggregation. By capturing higher-order gradient moments, PNCS addresses non-IID data challenges, enhancing convergence speed and accuracy. Additionally, we introduce a simple algorithm ensuring diverse client selection through a selection history queue. Experiments with a VGG16 model across varied data partitions demonstrate consistent improvements over state-of-the-art methods.

• The paper introduces the Power-Norm Cosine Similarity (PNCS) framework to address data heterogeneity and enhance diverse client selection in Federated Learning.
• It formulates client selection using a logistic regression model based on gradient differences and finds that L4-norm based cosine similarity is particularly effective for capturing gradient diversity.
• Empirical validation demonstrates that employing PNCS leads to significant improvements in convergence speed and accuracy on non-IID data compared to existing client selection methods.

Analysis of PNCS for Client Selection in Federated Learning

Federated Learning (FL) is increasingly utilized as a method for training models on decentralized data while upholding data privacy. However, the heterogeneity of local data across clients remains a major challenge, affecting convergence and communication efficiency. The paper "PNCS: Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning" introduces a novel framework to address these issues by proposing a strategic client selection mechanism using Power-Norm Cosine Similarity (PNCS).

The authors present the PNCS framework, which is designed to enhance client diversity in the aggregation process of the global model in a federated setup. This is particularly critical in scenarios involving non-IID data distributions where traditional methods might struggle. The paper's proposition revolves around capturing higher-order gradient moments using PNCS to select clients whose updates are more likely to provide complementary contributions to the global model.

Core Contributions

The research brings forth several key contributions:

1. Client Selection Problem Formulation: The authors redefine client selection as a logistic regression model. This model uses features based on pairwise gradient differences to decide the probability of potential client combinations for model updates. This novel approach allows for estimating the suitability of clients to participate in the training process, thus addressing one of the fundamental challenges in federated learning involving client and model update diversity.
2. Power-Norm Cosine Similarity (PNCS): By introducing the PNCS metric, the research identifies it as a robust feature in different data heterogeneity scenarios. Amongst various $L_{K}$-norm-based cosine similarity measures, $L_4$ emerged as particularly effective, capturing the nuances in gradient alignment and diversity.
3. Empirical Validation: Experiments using VGG16 across different data partitions illustrate significant improvements in convergence speed and accuracy when employing PNCS compared to existing strategies. These findings are substantiated by an empirical evaluation that includes extensive numerical tests.

Implications

From a theoretical standpoint, this paper contributes to the optimization strategies in federated learning by leveraging gradient diversity over similarity. The novelty of using higher-order norm-based similarities could inspire further inquiry into similar methods that consider more complex data and model interactions.

Practical implications of this research include its potential to significantly optimize communication costs and improve the accuracy of models trained in federated settings by allowing for more informed client selection. This is particularly pertinent for applications of FL in highly heterogeneous environments such as mobile edge computing and IoT applications where communication resources are limited and data privacy is paramount.

Future Prospects

The insights from this research open avenues for future exploration, such as adapting PNCS to more nuanced models or exploring its applicability in reinforcement learning paradigms where federated settings encounter different dynamics. Moreover, further refinement of selection algorithms to incorporate real-time data assessment can propel federated learning frameworks towards more agile and adaptive implementations.

The PNCS framework suggested by this research provides a meaningful advancement in tackling the obstacles of client diversity within federated learning systems. It underscores the significance of gradient diversity, pushing the discourse beyond traditional methods, and setting a precedent for future works in the domain of federated learning optimization.