Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without direct data sharing. However, existing frameworks struggle to simultaneously ensure strong privacy protection and robustness against adversarial participants. Although numerous robust aggregation algorithms can mitigate Byzantine attacks and various privacy-preserving approaches have been developed, achieving both objectives within a unified and efficient framework remains challenging. To address this compatibility gap, we propose a generic privacy-preserving FL framework that integrates robust aggregation with cryptographic privacy guarantees through secure two-party computation under a dual-server architecture. In our design, two non-colluding servers collaboratively execute the aggregation process, ensuring that sensitive client updates remain confidential while enabling the application of diverse robust aggregation strategies. The framework is flexible and supports a wide range of aggregation algorithms, such as Multi-Krum, Median, and Mean-based methods, under a consistent security model. We implement the proposed system and conduct extensive experiments on multiple benchmark datasets. Experimental results demonstrate that our framework preserves the effectiveness of existing robust aggregation algorithms while maintaining acceptable runtime and communication overhead compared with standard FL baselines. These findings confirm that the proposed approach provides a practical and balanced solution to the dual challenges of privacy protection and Byzantine robustness, offering a versatile foundation for secure and trustworthy FL in adversarial environments.

In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that speed up SVM decision. The method first obtains all the support vectors by classical SVM. Then, γ-index measuring the average distance between each support vector and its nearest neighbors is evaluated. Thirdly, the support vector with smallest γ-index is selected. And then iteratively re-weight γ-index and select only a few support vectors to represent all the support vectors. Our experiments show only 10%-30% of the support vectors can be used to speed up the decision while loss in generalization performance remains acceptable.

In BCI research community, EEG based self-paced brain-computer interfaces (SBCI) have been widely researched in the past several years. SBCI systems allow individuals to control outside device using EEG signals at their own pace. But the performance of current SBCI technology is not suitable for most applications due to the difficult in detection of the non-periodic intentionally brain state changing. In this paper, we propose a new feature selection method based on particle swarm optimization (PSO) for EEG-based motor-imagery (MI) SBCI systems. The method includes the following two steps: (1) an optimization algorithm, i.e. PSO is used to select the EEG features and classifier parameters; and (2) a voting mechanism is introduced to remove the features redundant, which produced by optimization algorithm. We also compare the proposed method with the genetic algorithm (GA) method. Experiment on single-trial MI EEG classification shows the effectiveness of the proposed method.

Delay-constrained streaming of fully-scalable video over IEEE 802.11a/e wireless (WLANs) is of great interest for many emerging multimedia applications. In this paper, we consider the problem of video transmission over HCF controlled channel access (HCCA), which is part of the new medium access control (MAC) protocol of IEEE 802.11e. A cross-layer optimization across the MAC and application layers is used in order to exploit the features provided by the new HCCA standard, as well as by the versatility of new state-of-the-art scalable video coding algorithms. Under pre-determined delay constraints for streaming, the proposed cross-layer strategy leads to a larger number of stations being simultaneously admitted (without any loss in the video quality) than in systems that utilize application-layer only optimizations. At the same time, the fine-grain layering provided by the scalable bitstream facilitates prioritization and unequal retransmissions of packets at the MAC layer thereby enabling graceful quality degradation under channel-capacity limitations and delay constraints. The expected gains offered by the optimized solutions proposed in this paper are established through simulations

The quality-of-service (QoS) guarantees enabled by the new IEEE 802.11 a/e Wireless LAN (WLAN) standard are specifically targeting the real-time transmission of multimedia content over the wireless medium. Since video data consume the largest part of the available bitrate compared to other media, optimization of video streaming for this new standard is a significant factor for the successful deployment of practical systems. Delay-constrained streaming of fully-scalable video over IEEE 802.11 a/e WLANs is of great interest for many multimedia applications. The new medium access control (MAC) protocol of IEEE 802.11e is called the Hybrid Coordination Function (HCF) and, in this paper, we will specifically consider the problem of video transmission over HCF Controlled Channel Access (HCCA). A cross-layer optimization across the MAC and application layers of the OSI stack is used in order to exploit the features provided by the combination of the new HCCA standard with new versatile scalable video coding algorithms. Specifically, we propose an optimized and scalable HCCA-based admission control for delay-constrained video streaming applications that leads to a larger number of stations being simultaneously admitted (without quality reduction to any video flow). Subsequently, given the allocated transmission opportunity, each station deploys an optimized Application-MAC-PHY adaptation, scheduling, and protection strategy that is facilitated by the fine-grain layering provided by the scalable bitstream. Given that each video flow needs to always comply with the predetermined (a priori negotiated) traffic specification parameters, this cross-layer strategy enables graceful quality degradation whenever the channel conditions or the video sequence characteristics change. For instance, it is demonstrated that the proposed cross-layer protection and bitstream adaptation strategies facilitate QoS token rate adaptation under link adaptation mechanisms that utilize different physical layer transmission rates. The expected gains offered by the optimized solutions proposed in this paper are established theoretically, as well as through simulations.