Federated Learning has emerged as a decentralized method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while keeping their data stored locally. However, a significant challenge arises when dealing with missing modalities in clients’ datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. Previous studies have addressed this issue, but they fall short in addressing generalizability across diverse, unobserved individuals. This study introduces MIFL, a novel framework for handling modality-missing clients in Multimodal Federated Learning. MIFL utilizes a unique approach, optimizing a client’s local model with existing modalities while incorporating absent modalities from clients. Aggregation of these models is performed through a graph-based attentive aggregation method, maintaining generalized characteristics by updating a global model averaged across clients. Our experimental results demonstrate the effectiveness of MIFL across various client configurations with statistical heterogeneity, showcasing its potential for addressing the challenge of missing modalities in Federated Learning.

Due to global air pollution , there is a growing demand for accurate and large-scale air quality monitoring systems. Consequently, low-cost air monitoring devices have emerged as a potential alternative to expensive conventional ones. However, the low-cost devices’ major drawback is their insufficient level of accuracy. This work investigates the problem of calibrating the sensory data, especially PM2.5 concentration, collected by low-cost sensor-based air quality monitoring devices. Recently, deep learning has emerged as a potential solution for data calibration instead of using traditional methods, whose accuracy is relatively low. Nevertheless, it generally incurs significant costs. Moreover, it is necessary to employ a dedicated calibration model for each device to increase precision, resulting in additional expenditures. To address the issue, this study provides a novel approach named GAMMA, which entails the development of a deep learning-based model capable of accurately calibrating data for multiple devices simultaneously. The proposed method leverages the multitask learning paradigm to solve the challenge of concurrently processing several devices’ data. This involves capturing common features across all devices’ data while also distinguishing the device-specific characteristics. Furthermore, GAMMA also employs the adversarial training approach to augment the accuracy of predictions. This method has been implemented and integrated into an air quality monitoring system in Hanoi, Vietnam. Comprehensive experiments are conducted on real-world data to demonstrate the superiority of GAMMA against the comparison benchmarks in terms of various metrics. Notably, GAMMA reduces MAE from 60.19% to 74.09% compared to the best comparison baseline. The source code is available at https://github.com/anhduy0911/FimiCalibIdea/tree/multi_attention .