Joint Compression and Deadline Optimization for Wireless Federated Learning

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes.Comment: 13 pages, accepted by IEEE Transactions on Mobile Computing (TMC

Performance of plain and slag-blended cements and mortars exposed to combined chloride-sulphate solution

The durability of reinforced concrete structures exposed to aggressive environments remains a challenge to both researchers and the construction industry. This study investigates the hydration, mechanical properties and durability characteristics of ground granulated blast-furnace slag (GGBS) - blended cements and mortars exposed to a combined sodium chloride - sulphate environment, at temperatures of 20°C and 38°C. The conditions were chosen so as to assess the performance of slag blends under typical temperate and warm tropical marine climatic conditions. Slags, having CaO/SiO2 ratios of 1.05 and 0.94, were blended with CEM I 52.5R at 30% replacement level to study the influence of slag composition and temperature. Parallel control tests were carried out with CEM I 42.5R. Pastes and mortar samples were cast using 0.5 water to binder ratio, pre-cured for 7 days in water before exposure. Flexural strengths were determined once the samples were 7, 28 or 90 days old. Hydration was followed using x-ray diffraction (XRD), thermal analysis, and calorimetry. Also, sorptivity, gas permeability and chloride diffusion tests were carried out on mortar samples to measure transport and durability characteristics. The results show improved mechanical and transport properties for slag blended cements exposed to environments rich in sodium chloride and sulphate