Multi-Center Federated Learning: Clients Clustering for Better Personalization (2108.08647v4)

Abstract: Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision-making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models' parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository https://github.com/mingxuts/multi-center-fed-learning .

• The paper introduces a client clustering method in multi-center federated learning to enhance personalization.
• It highlights how missing PDFs and source files on arXiv impede timely access and research dissemination.
• The study calls for robust archival protocols and AI-driven tools to ensure continuous, reliable academic access.

Analyzing Manuscript Accessibility on arXiv

The metadata provided offers a brief glimpse into challenges associated with the availability of certain manuscripts on preprint servers like arXiv, focusing on a document identified by the reference number (Long et al., 2021 )v4. As the PDF and source file for this particular submission are unavailable, this scenario presents an opportunity to reflect on the broader implications of manuscript accessibility for the research community.

The absence of a downloadable PDF or source file for the mentioned submission exemplifies crucial issues regarding the dissemination of scientific knowledge. It raises concerns about:

1. Access to Information: Researchers rely on repositories like arXiv for timely access to cutting-edge research. The lack of a download option impedes scientific communication, potentially delaying advancements and stifling scholarly interaction.
2. Technical Limitations: The unavailability can stem from technical issues on the author's or platform’s side. Understanding and mitigating these barriers are essential for enhancing the efficiency of academic dissemination systems.
3. Archival Completeness: Ensuring complete archives of submissions can facilitate longitudinal studies, meta-analyses, and the verification of research findings. The absence of certain documents poses challenges for maintaining comprehensive scholarly records.

In terms of practical and theoretical implications, an emphasis on robustness in preprint repository systems could lead to protocols ensuring consistent document availability. Enhanced guidelines or systems might be developed to automatically verify and rectify submission upload issues, thereby preserving the integrity of academic archives.

The future developments in AI and machine learning could potentially address such challenges through automated tools that detect and resolve document-formatting issues in real-time. Furthermore, AI-driven solutions might offer alternative access solutions, such as generating text-readable versions of manuscripts directly from the database inputs, ensuring that unavailability due to formatting restrictions is minimized.

In conclusion, while this specific case of document unavailability is not uncommon, it serves as a crucial reminder of the importance of reliable access to scholarly communication. It emphasizes the need for continual improvements in the infrastructure of digital repositories to foster uninterrupted academic discourse. As the landscape of AI and its applications continues to evolve, they could play a central role in advancing the operational efficacy of platforms like arXiv, ultimately benefiting the global research community.