Multi-modal learning leverages data from diverse perceptual media to obtain enriched representations, thereby empowering machine learning models to complete more complex tasks. However, recent research results indicate that multi-modal learning still suffers from " modality imbalance '': Certain modalities' contributions are suppressed by dominant ones, consequently constraining the overall performance enhancement of multimodal learning. To tackle this issue, current approaches attempt to mitigate modality competition in various ways, but their effectiveness is still limited. To this end, we propose an Euler Representation Learning-based Modality Rebalance (ERL-MR) strategy, which reshapes the underlying competitive relationships between modalities into mutually reinforcing win-win situations while maintaining stable feature optimization directions. Specifically, ERL-MR employs Euler's formula to map original features to complex space, constructing cooperatively enhanced non-redundant features for each modality, which helps reverse the situation of modality competition. Moreover, to counteract the performance degradation resulting from optimization drift among modalities, we propose a Multi-Modal Constrained (MMC) loss based on cosine similarity of complex feature phase and cross-entropy loss of individual modalities, guiding the optimization direction of the fusion network. Extensive experiments conducted on four multi-modal multimedia datasets and two task-specific multi-modal multimedia datasets demonstrate the superiority of our ERL-MR strategy over state-of-the-art baselines, achieving modality rebalancing and further performance improvements.

Intrusion Detection Systems (IDS) integrated with Machine Learning (ML) techniques have proven to be effective defenses against the increasing cybersecurity attacks in the Artificial Intelligence of Things (AIoT) domain. Privacy concerns have prompted the emergence of Federated Learning (FL) as a promising solution for AIoT intrusion detection. Despite their potential, FL-based IDSs still face challenges related to class-imbalanced data and Non-Independent and Identically Distributed (non-IID) data among AIoT devices. These challenges hinder FL from learning meaningful features from the data, thus impeding the convergence of the learning process. To tackle these issues, this paper proposes a Clustering-enabled Federated Meta-Training (CFMT) framework for AIoT intrusion detection. The proposed CFMT framework effectively addresses the negative impact of imbalanced and non-IID data. Specifically, we design a data-and model-agnostic meta-sampler that adaptively balances local datasets, thereby mitigating the data imbalance problem. Additionally, we propose a dynamic clustering algorithm that selectively eliminates the local models affected by the training state bias caused by non-IID data, thereby addressing the non-IID data issue. Extensive case studies on two real-world datasets demonstrate the superior performance of the proposed CFMT framework compared to existing solutions, including federated non-IID algorithms and federated imbalanced learning algorithms, in terms of IDS performance. Our code and data are available at https://gitee.com/mindspore/models/tree/master/research/cv/HDFL-IDS-Meta.