Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients

Federated optimization, an emerging paradigm which finds wide real-world applications such as federated learning, enables multiple clients (e.g., edge devices) to collaboratively optimize a global function. The clients do not share their local datasets and typically only share their local gradients. However, the gradient information is not available in many applications of federated optimization, which hence gives rise to the paradigm of federated zeroth-order optimization (ZOO). Existing federated ZOO algorithms suffer from the limitations of query and communication inefficiency, which can be attributed to (a) their reliance on a substantial number of function queries for gradient estimation and (b) the significant disparity between their realized local updates and the intended global updates. To this end, we (a) introduce trajectory-informed gradient surrogates which is able to use the history of function queries during optimization for accurate and query-efficient gradient estimation, and (b) develop the technique of adaptive gradient correction using these gradient surrogates to mitigate the aforementioned disparity. Based on these, we propose the federated zeroth-order optimization using trajectory-informed surrogate gradients (FZooS) algorithm for query- and communication-efficient federated ZOO. Our FZooS achieves theoretical improvements over the existing approaches, which is supported by our real-world experiments such as federated black-box adversarial attack and federated non-differentiable metric optimization

Bis[μ-1,2-diphenyl-N,N′-bis­(di-2-pyridyl­methyl­eneamino)ethane-1,2-diimine]disilver(I) bis­(hexa­fluorido­phosphate) acetonitrile disolvate

In the centrosymmetric dinuclear title compound, [Ag2(C36H26N8)2](PF6)2·2C2H3N, the Ag+ ion is bound to four N atoms from two 1,2-diphenyl-N,N′-bis­(di-2-pyridyl­methyl­eneamino)ethane-1,2-diimine ligands in a distorted tetra­hedral geometry. The ligand adopts a twist conformation, coordinating two metal centers by three pyridyl N atoms and one imine N atom and spanning two Ag+ ions, resulting in the formation of a helical dimeric structure

Fair yet Asymptotically Equal Collaborative Learning

In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a "rich get richer" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/"rich" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality.Comment: Accepted to 40th International Conference on Machine Learning (ICML 2023), 37 page

Substrate Temperature Effect on the Microstructure and Properties of (Si, Al)/a-C:H Films Prepared through Magnetron Sputtering Deposition

Hydrogenated amorphous carbon films codoped with Si and Al ((Si, Al)/a-C:H) were deposited through radio frequency (RF, 13.56 MHz) magnetron sputtering on Si (100) substrate at different temperatures. The composition and structure of the films were investigated by means of X-ray photoelectron spectroscopy (XPS), TEM, and Raman spectra, respectively. The substrate temperature effect on microstructure and mechanical and tribological properties of the films was studied. A structural transition of the films from nanoparticle containing to fullerene-like was observed. Correspondingly, the mechanical properties of the films also had obvious transition. The tribological results in ambient air showed that high substrate temperature (>573 K) was disadvantage of wear resistance of the films albeit in favor of formation of ordering carbon clusters. Particularly, the film deposited at temperature of 423 K had an ultralow friction coefficient of about 0.01 and high wear resistance