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Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data. One challenge associated with FL is statistical diversity among clients, which restricts the global model from delivering good performance on each client's task. To address this, we propose an algorithm for personalized FL (pFedMe) using Moreau envelopes as clients' regularized loss functions, which help decouple personalized model optimization from the global model learning in a bi-level problem stylized for personalized FL. Theoretically, we show that pFedMe's convergence rate is state-of-the-art: achieving quadratic speedup for strongly convex and sublinear speedup of order 2/3 for smooth nonconvex objectives. Experimentally, we verify that pFedMe excels at empirical performance compared with the vanilla FedAvg and Per-FedAvg, a meta-learning based personalized FL algorithm.

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Remarkable adsorption enhancement and packing of dilute mixtures of water-soluble oppositely-charged surfactants, sodium dodecyl sulfate (SDS) and dodecyl amine hydrochloride (DAH), at the air-water interface were observed by using sum frequency generation spectroscopy and tensiometry. The interfacial water structure was also observed to be significantly influenced by the SDS-DAH mixtures, differently from the synergy of the single surfactants. Most strikingly, the obtained spectroscopic evidence suggests that the interfacial hydrophobic alkyl chains of the binary mixtures assemble differently from those of single surfactants. This study highlights the significance of the cooperative interaction between the headgroups of oppositely charged binary surfactant systems and subsequently provides some insightful observations about the molecular structure of the air-aqueous interfacial water molecules and, more importantly, about the packing nature of the surfactant hydrophobic chains of dilute SDS-DAH mixtures of concentration below 1% of the CMC.

as core materials for future spintronics given their potential for high Curie temperature (T C ) and efficient field-tunable magnetic properties. [1][2][3] Despite half-century-long research efforts, the following three primary issues remain unsolved: i) the uncertain origin of ferromagnetism, called phantom ferromagnetism, owing to a lack of structural analysis of nanodefects; [4,5] ii) solubility limit to only a few percent without forming aggregation; [6] and iii) activation of short-range antiferromagnetic transitions in the high-dopingconcentration regime, [7] which limits the improvement of the magnetic moment and T C .The recent emergence of magnetic order in 2D van der Waals layered materials, which is enabled by strong magnetic anisotropy, [8] has stimulated interest in 2D-DMSs owing to their exotic spindependent physical properties, including long spin-relaxation time, light-controlled magnetism, [9] and spin-valley locking, inherent to their atomically thin nature. [10][11][12] In particular, transition metal dichalcogenide (TMD) semiconductors with magnetic dopants synthesized via chemical vapor deposition (CVD) offer room-temperature T C and gate-tunable magnetism. [13][14][15] Although vanadium dopants in WSe 2 and WS 2 semiconductors have been successfully distributed randomly without aggregation to a relatively high doping concentration of approximately 10%, their saturation magnetization is still limited to approximately 10 −5 emu cm −2 , thus making further analysis and applications difficult. [14,15] While magnetism has been proposed for inducing various defects such as vacancies, [16] anti-sites, [17] and grain boundaries [18] in III-V, oxides, and nitride DMSs, the underlying mechanism of magnetism is little known mainly due to the lack of structural analysis. On the contrary, because of facile monolayer growth, a variety of defects, including transition metal and chalcogen vacancies in 2D-TMDs, can be precisely analyzed using state-of-the-art scanning transmission electron microscopy (STEM) with atomic elemental mapping. [19] This affords the possibility of elucidating the origins of magnetism from defects and further enhancing magnetic order by tailoring intrinsic defects and impurities in 2D-TMD semiconductors. Here, we present a comprehensive atomic analysis of Se-vacancy defects Magnetic order has been proposed to arise from a variety of defects, including vacancies, antisites, and grain boundaries, which are relevant in numerous electronics and spintronics applications. Nevertheless, its magnetism remains controversial due to the lack of structural analysis. The escalation of ferromagnetism in vanadium-doped WSe 2 monolayer is herein demonstrated by tailoring complex configurations of Se vacancies (Se Vac ) via post heat-treatment. Structural analysis of atomic defects is systematically performed using transmission electron microscopy (TEM), enabled by the monolayer nature. Temperature-dependent magnetoresistance hysteresis ensures enhanced magnetic order after high-temperature heat-tre...