Rating aggregation plays a crucial role in various fields, such as product recommendations, hotel rankings, and teaching evaluations. However, traditional averaging methods can be affected by participation bias, where some raters do not participate in the rating process, leading to potential distortions. In this paper, we consider a robust rating aggregation task under the participation bias. We assume that raters may not reveal their ratings with a certain probability depending on their individual ratings, resulting in partially observed samples. Our goal is to minimize the expected squared loss between the aggregated ratings and the average of all underlying ratings (possibly unobserved) in the worst-case scenario.

Non-identically distributed (Non-IID) data is a ma-jor challenge in federated learning (FL). Although many related studies have proposed methods to improve FL model performance, they often incur significant resource consumption. These studies typically save gradient states for training correction, some requiring clients to synchronize these states. Given that clients' extra gradient states could be substantial, even several times larger than the model's size, maintaining and synchronizing such large-size gradient states consume considerable memory and communication resources. This paper rigorously explores a substantial reduction in Non-IID methods' resource consumption on clients by reconstructing Non-IID methods' local corrections on the server. A crucial insight driving this reconstruction is to ensure symmetrical execution time for corrections. Motivated by this principle, we introduce Fleet, a lightweight FL framework. Fleet's server performs a two-stage symmetric gradient correction, while clients perform original gradient descents. Experimen-tal results demonstrate Fleet's superior performance over state-of-the-art methods, with resource consumption comparable to lightweight FedAvg on clients. Especially, Fleet excels in training deep models using large datasets. The experimental findings also support Fleet's dynamic scheduling as a plug-and-play module, showcasing its practical potential in real-world applications.

Federated Learning (FL) is a promising distributed machine learning framework that allows clients to collaboratively train a global model without data leakage. The synchronous FL suffers from the inefficient training caused by the slow-speed clients, which are called stragglers. Though asynchronous FL can well address the efficiency challenge, it induces massive system overheads and model degradation. As a framework considering the trade-off between synchronous and asynchronous FL, semi-asynchronous FL gains increasing attention. However, when clients' resources become a bottleneck, an unfair client scheduling may degrade global training accuracy and increase system overheads, especially in heterogeneous environments. In this paper, we propose Libra, which is a new FL framework aiming to achieve fair client scheduling in semi-asynchronous FL mode. Libra restricts devices that train too fast according to the model discrepancy. Furthermore, it selects stale local models according to the number of participating into FL training by clients. Additionally, Libra conducts a biased client selection while considering clients' resources and local losses. The experimental results show that Libra outperforms other baselines in terms of convergence accuracy, system overhead, and fairness of client participation. We also conduct an ablation study to further prove the effectiveness of Libra. In brief, Libra can achieve fair client scheduling and reduce inefficient local updates.

Federated learning (FL) has been widely used in IoT applications. However, FL is vulnerable to various attacks in its each phase. Existing defense in federated learning mainly focus on the centralized setting. And centralized parameter-server settings require a trusted third party to collect and distribute model parameters. However, the requirement of a trusted third party cannot always be satisfied in many cases. Meanwhile, the centralized settings suffer from the inherent vulnerability of single-point-of-failure (SPOF), in which the whole system cease to function once the parameter server is broken. Therefore, decentralized federated learning has gain great attention recently. Existing conventional defense strategies are mostly designed for the centralized parameter-server architecture. The problem is that these conventional defense strategies cannot cope with new challenges occurred in highly decentralized settings of FL. Firstly, in a trustless setting, malicious participants can cause breakdown to the whole system on the communication level by disrupting model exchanges. Secondly, current defensive methods cannot effectively identify and rule out malicious participants. In either case, a harmful bias hurts the performance even if malicious participants do not perform model-level attacks. Therefore, defensive strategies for the decentralized-manner FL are in urgent need. To this end, we propose a committee-based FL system , named ComAvg , under a trustless setting. ComAvg provides a general coordination scheme for robust aggregation of distributed learning . With reliability assessment scheme to expel abnormal participants and fortified classic model exchange methods, the conventional centralized methods of FL can be easily modified into decentralized versions to cope with the two challenges aforementioned. Finally, we implement a prototype of ComAvg and perform various groups of evaluations on its robustness. The prototype-based evaluation results and theoretical analysis show that the proposed ComAvg is effective against model attacks such as sign-flipping and communication-level isolating attacks.

Federated Learning (FL) suffers from Low-quality model training in mobile edge computing, due to the dynamic environment of mobile clients. To the best of our knowledge, most FL frameworks follow the reactive client scheduling, in which the FL parameter server selects participants according to the currently-observed state of clients. Thus, the participants selected by the reactive-manner methods are very likely to fail while training a round of FL. To this end, we propose a proactive Context-aware Federated Learning (ContextFL) mechanism, which consists of two primary modules. Firstly, the state prediction module enables each client device to predict the conditions of both local training and reporting phases of FL locally. Secondly, the decision-making algorithm module is devised using the contextual Multi-Armed Bandit (cMAB) framework, which can help the parameter server select the most appropriate group of mobile clients. Finally, we carried out trace-driven FL experiments using real-world mobility datasets collected from volunteers. The evaluation results demonstrate that the proposed ContextFL mechanism outperforms other baselines in terms of the convergence stability of the global FL model and the ratio of valid participants.

Adaptive Block-size Transform (ABT) has been added to H.264/AVC standard with the Fidelity Range Extension. In this paper, we apply this ABT concept to our FME design and propose a full-mode FME architecture based on 8×8/4×4 adaptive Hadamard Transform. This technique can avoid unifying all variable block-size blocks into 4×4-size blocks and improve the encoding performance. We also exploit the linearity of Hadamard Transform in quarter-pel refinement and decrease the cycles caused by the second long search process. In architecture level, we employ two interpolating engines that can support 8-pel and 4-pel input to time-share one SATD (Sum of Absolute Hadamard Transform) Generator. These strategies can increase parallelism and reduce the cycles efficiently. Besides, this design can support full modes, which guarantees the encoding performance. Experimental results show that our design can achieve real-time processing for QFHD@30fps at the operation frequency of 320MHz with 444.6K gates hardware.