Advances in deep learning have transformed medical imaging, yet progress is hindered by data privacy regulations and fragmented datasets across institutions. To address these challenges, we propose FedVGM, a privacy-preserving federated learning framework for multi-modal medical image analysis. FedVGM integrates four imaging modalities, including brain MRI, breast ultrasound, chest X-ray, and lung CT, across 14 diagnostic classes without centralizing patient data. Using transfer learning and an ensemble of VGG16 and MobileNetV2, FedVGM achieves 97.7% $\pm$ 0.01 accuracy on the combined dataset and 91.9-99.1% across individual modalities. We evaluated three aggregation strategies and demonstrated median aggregation to be the most effective. To ensure clinical interpretability, we apply explainable AI techniques and validate results through performance metrics, statistical analysis, and k-fold cross-validation. FedVGM offers a robust, scalable solution for collaborative medical diagnostics, supporting clinical deployment while preserving data privacy.

Quaternion algebra has been used to apply the fractional Fourier transform (FrFT) to color images in a comprehensive approach. However, the discrete fractional random transform (DFRNT) with adequate basic randomness remains to be examined. This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform (DQFrFT) watermarking and three-dimensional chaotic logistic map (3D-CLM) encryption. First, we describe quaternion DFRNT (QDFRNT), which generalizes DFRNT to handle quaternion signals effectively, and then use QDFRNT to perform color medical image adaptive watermarking. To efficiently evaluate QDFRNT, this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal. Moreover, it uses the human vision system’s (HVS) masking qualities of edge, texture, and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine (SVM) techniques. The limitations of watermark embedding are also explained to conserve watermarking energy. Second, 3D-CLM encryption is employed to improve the system’s security and efficiency, allowing it to be used as a multistage privacy system. The proposed security system is effective against many types of channel noise attacks, according to simulation results.

Abstract Millimetre wave (mmWave) communications, that is, 30 to 300 GHz, have intermittent short‐range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mmWave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two‐phase multi‐armed bandit (MAB) game. In the first phase, the PS vector of the mmWave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy (MOSS) MAB algorithm is utilized to implement the proposed two‐phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information (CSI) of both mmWave BS and RIS, codebook‐based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios.

The authors propose efficient hybrid error resilience and error concealment (ER‐EC) algorithms for H.264 3D video‐plus‐depth (3DV + D) transmission over error‐prone channels. At the encoder, content‐adaptive pre‐processing ER mechanisms are implemented by applying the context adaptive variable length coding (CAVLC), the slice structured coding, and the explicit flexible macro‐block ordering. At the decoder, a post‐processing EC algorithm with multi‐proposition schemes is implemented to recover the lost 3DV colour frames. The convenient EC hypothesis is adopted based on the lost macro‐blocks size mode, the faulty view, and the frame types. For the recovery of the lost 3DV depth frames, an encoder‐independent decoder‐dependent depth‐assisted EC algorithm is suggested. It exploits the previously estimated colour disparity vectors (DVs) and motion vectors (MVs) to estimate more additional depth‐assisted MVs and DVs. After that, the optimum colour‐plus‐depth DVs and MVs are accurately selected by employing the directional interpolation EC algorithm and the decoder MV estimation algorithm. Finally, a weighted overlapping block motion and disparity compensation scheme is utilised to reinforce the performance of the proposed hybrid ER‐EC algorithms. Experimental results on standard 3DV + D sequences show that the proposed hybrid algorithms have superior objective and subjective performance indices.

Efficiently compressing 3D multi-view video, while maintaining a high quality of received 3D video, is very challenging. Error Concealment (EC) algorithms have the advantage of improving the received video quality without modifications in the transmission rate or in the encoder hardware or software. To improve the quality of reconstructed 3D multi-view video, we propose different algorithms to conceal the erroneous and lost blocks of intra-coded and inter-coded frames by exploiting the spatial, temporal and inter-view correlations between frames and views. A hybrid of Space Domain Error Concealment (SDEC) and Time Domain Error Concealment (TDEC) is introduced for concealment of intra-frames errors. Three EC modes are introduced for inter-frames, which are Time Domain Error Concealment (TDEC), Inter-view Domain Error Concealment (IVDEC) and joint Time and Interview Domain Error Concealment (TIVDEC). Our simulation results show that the proposed algorithms can significantly improve the objective and subjective quality of reconstructed 3D multi-view video sequences.