Fractional-Order Federated Learning
arXiv:2602.15380v1 Announce Type: new Abstract: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability caused by heterogeneous, non-IID client data. We compare FOFedAvg against a broad set of established federated optimization algorithms on benchmark datasets including MNIST, FEMNIST, CIFAR-10, CIFAR-100, EMNIST, the Cleveland heart disease d
arXiv:2602.15380v1 Announce Type: new Abstract: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability caused by heterogeneous, non-IID client data. We compare FOFedAvg against a broad set of established federated optimization algorithms on benchmark datasets including MNIST, FEMNIST, CIFAR-10, CIFAR-100, EMNIST, the Cleveland heart disease dataset, Sent140, PneumoniaMNIST, and Edge-IIoTset. Across a range of non-IID partitioning schemes, FOFedAvg is competitive with, and often outperforms, these baselines in terms of test performance and convergence speed. On the theoretical side, we prove that FOFedAvg converges to a stationary point under standard smoothness and bounded-variance assumptions for fractional order $0<\alpha\le 1$. Together, these results show that fractional-order, memory-aware updates can substantially improve the robustness and effectiveness of federated learning, offering a practical path toward distributed training on heterogeneous data.
Executive Summary
This article presents a novel federated learning algorithm called Fractional-Order Federated Averaging (FOFedAvg) that incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to improve communication efficiency and accelerate convergence in heterogeneous, non-IID client data. The authors demonstrate the effectiveness of FOFedAvg compared to established baselines on various benchmark datasets and provide theoretical convergence guarantees under standard smoothness and bounded-variance assumptions. The results suggest that fractional-order, memory-aware updates can substantially improve the robustness and effectiveness of federated learning, offering a practical path toward distributed training on heterogeneous data.
Key Points
- ▸ FOFedAvg incorporates FOSGD to capture long-range relationships and deeper historical information
- ▸ FOFedAvg improves communication efficiency and accelerates convergence
- ▸ FOFedAvg converges to a stationary point under standard smoothness and bounded-variance assumptions
Merits
Strength in Handling Non-IID Data
FOFedAvg effectively mitigates instability caused by heterogeneous, non-IID client data, offering a practical solution for real-world federated learning applications.
Improved Convergence Speed
FOFedAvg accelerates convergence compared to established baselines, making it a valuable addition to the federated learning toolkit.
Theoretical Guarantees
The authors provide theoretical convergence guarantees for FOFedAvg under standard smoothness and bounded-variance assumptions, adding credibility to the algorithm's effectiveness.
Demerits
Limited Exploratory Analysis
The article primarily focuses on benchmark datasets and does not extensively explore the algorithm's performance in real-world applications or on larger datasets.
Assumptions and Simplifications
The authors rely on standard smoothness and bounded-variance assumptions, which might not hold in all real-world scenarios, highlighting the need for further research on the algorithm's robustness.
Expert Commentary
The article presents a novel and innovative approach to federated learning, offering a promising solution to the challenges of non-IID data and heterogeneous client environments. The theoretical guarantees and empirical results demonstrate the effectiveness of FOFedAvg, making it a valuable addition to the federated learning toolkit. However, further research is needed to explore the algorithm's robustness and scalability in real-world applications. Additionally, policymakers should prioritize research and development in the area of federated learning to address the growing need for distributed and private AI solutions.
Recommendations
- ✓ Further research should focus on exploring the algorithm's robustness and scalability in real-world applications, particularly on larger datasets and in more complex client environments.
- ✓ Policymakers should prioritize research and development in the area of federated learning, emphasizing the development of algorithms that can effectively handle non-IID data and heterogeneous client environments.
