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Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

A high-capacitive frequency selective surface (FSS) with a new structure of folded spiral conductors is proposed as the small-array periodicity and low-frequency resonance FSS for ultra-wide bandwidth absorbers in a multilayer structure. Due to the folded structure with long effective segments and a small gap, a large value of capacitance for the lowest resonating frequency is obtained. Through a combination of the high-capacitive spiral FSS with other conventional FSSs (square loop, square patch) with a medium- and high-frequency resonance, an ultra-wide absorption bandwidth (4.7–50.0 GHz for −10 dB reflection loss) is designed with a small total thickness of 7.0 mm, which is close to the theoretical limit (6.7 mm). Admittance analysis is conducted for better insight into the optimization procedure. The free space measurement with a test sample prepared by the screen printing method also demonstrates a wide-bandwidth absorption result (5.2–44.0 GHz for −10 dB reflection loss, total thickness = 6.5 mm), which is in good agreement with the simulation result. In addition, the angular stability of the proposed wide-bandwidth absorber is discussed for both TE and TM polarizations in association with unit cell periodicity and grating lobes.

The asymmetry of heat conduction in thermal diodes is attributed to the thermal regulation in thermal logic circuits, thermal control devices, etc. Currently, thermal rectification devices, however, are constrained by either specific on/off switching thresholds, or low sensitivity to manufacturing errors, as well as the restrictions for specific roughness and material characteristics. Here, a thermal diode based on fractal structures (inverted tapered branch channel, 5.7 mm branch length, and 4 branches) with liquid metal/insulator interface is proposed to avoid these existing constraints. The thermal rectification coefficient reaches 0.4704 (16.43 times larger than that of the basic structures) when the filling fraction only increases 1.33 times. The maximum temperature difference for the optimized thermal diodes is 18.31 °C when the heating temperature is 80 °C. By different arrangements of two thermal diodes mounted on chambers' surfaces, the interior temperatures range from 16.06 to 52.34 °C in the same ambient conditions (23.49 °C), indicating the excellent thermal regulation effect of the thermal diodes. These controlled temperature chambers can be applied in greenhouses or cold chain storage devices, more combinations with higher degree of thermal-threshold tunability offer possibilities in solving complicated thermal management problems and promote the energy utilization efficiency.

Because of its promising applications in various fields such as in vivo drug treatment, in-pipe inspection, and so forth, there is an increasing interest on wireless soft robot boats taking advantages of their shape adaptability. The loading capacity and mobility, however, are always fundamental challenges to restrict their applications. In this study, a graphene-based soft robot boat, which could be programmable-driven by a remote nearinfrared light, is proposed. Different microstructures underneath the boat are carefully designed and employed to improve both the loading capacity and the moving ability. It reveals that, compared to that without microstructures, the soft robot boat with square pillar arrays (120−160 μm of period, duty cycle, and aspect ratio at active Wenzel/Cassie transition point) could enhance the loading capacity by 12.75% and the moving velocity by 16.70%. For the robot boat with grating structures, a strong driving anisotropy is revealed, with an enhancement of 2.24% for the loading capacity and 34.65% for the driving response along the grating lines. A boat prototype with a self-weight of 6.05 g is finally developed and can achieve continuous navigation in a closed narrow space for in situ monitoring, which may find applications in the inspection of other narrow terrains (e.g., blood vessels).