FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate access to users' raw data, posing substantial privacy risks. While Federated Learning (FL) serves as a privacy-preserving alternative, it falls short in addressing the challenges posed by heterogeneous (yet possibly overlapped) modalities data across various hospitals. To bridge this gap, we propose a Federated Multi-Modal (FedMM) learning framework that federatedly trains multiple single-modal feature extractors to enhance subsequent classification performance instead of existing FL that aims to train a unified multimodal fusion model. Any participating hospital, even with small-scale datasets or limited devices, can leverage these federated trained extractors to perform local downstream tasks (e.g., classification) while ensuring data privacy. Through comprehensive evaluations of two publicly available datasets, we demonstrate that FedMM notably outperforms two baselines in accuracy and AUC metrics.

A Survey on Modern Recommendation System based on Big Data

Recommendation systems have become very popular in recent years and are used in various web applications. Modern recommendation systems aim at providing users with personalized recommendations of online products or services. Various recommendation techniques, such as content-based, collaborative filtering-based, knowledge-based, and hybrid-based recommendation systems, have been developed to fulfill the needs in different scenarios. This paper presents a comprehensive review of historical and recent state-of-the-art recommendation approaches, followed by an in-depth analysis of groundbreaking advances in modern recommendation systems based on big data. Furthermore, this paper reviews the issues faced in modern recommendation systems such as sparsity, scalability, and diversity and illustrates how these challenges can be transformed into prolific future research avenues.