Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient

Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss function is updated, amplified and then transmitted by every mobile device; the server receives the aggregated gradient all-at-once, generates and then broadcasts updated model parameters to every mobile device. In terms of amplification factor selection, most related works suppose the local gradient's maximal norm always happens although it actually fluctuates over iterations, which may degrade convergence performance. To circumvent this problem, we propose to turn local gradient to be normalized one before amplifying it. Under our proposed method, when the loss function is smooth, we prove our proposed method can converge to stationary point at sub-linear rate. In case of smooth and strongly convex loss function, we prove our proposed method can achieve minimal training loss at linear rate with any small positive tolerance. Moreover, a tradeoff between convergence rate and the tolerance is discovered. To speedup convergence, problems optimizing system parameters are also formulated for above two cases. Although being non-convex, optimal solution with polynomial complexity of the formulated problems are derived. Experimental results show our proposed method can outperform benchmark methods on convergence performance

Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap

In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks. At each user, model parameter is firstly updated by multiple steps, which is adjustable over iterations, and then pushed to the aggregation center directly. This decreases the number of interactions between the aggregation center and users, allows each user to set training parameter in a flexible way, and reduces computation burden compared with existing works that need to combine multiple historical model parameters. At the aggregation center, geometric median is leveraged to combine the received model parameters from each user. Rigorous proof shows that zero optimality gap is achieved by our proposed method with linear convergence, as long as the fraction of Byzantine attackers is below half. Numerical results verify the effectiveness of our proposed method

Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning

Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the training model parameter of every mobile device at one base station (BS) iteratively}. For parameter aggregating in FL, over-the-air computation is a spectrum-efficient solution, which allows all mobile devices to transmit their parameter-mapped signals concurrently to a BS. Due to heterogeneous channel fading and noise, there exists difference between the BS's received signal and its desired signal, measured as the mean-squared error (MSE). To minimize the MSE, we propose to jointly optimize the signal amplification factors at the BS and the mobile devices as well as the data size (the number of data samples involved in local training) at every mobile device. The formulated problem is challenging to solve due to its non-convexity. To find the optimal solution, with some simplification on cost function and variable replacement, which still preserves equivalence, we transform the changed problem to be a bi-level problem equivalently. For the lower-level problem, optimal solution is found by enumerating every candidate solution from the Karush-Kuhn-Tucker (KKT) condition. For the upper-level problem, the optimal solution is found by exploring its piecewise convexity. Numerical results show that our proposed method can greatly reduce the MSE and can help to improve the training performance of FL compared with benchmark methods

Skeleton Supervised Airway Segmentation

Fully-supervised airway segmentation has accomplished significant triumphs over the years in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-level annotation constitutes a labor-intensive and time-consuming task, often plagued by issues such as missing branches, branch annotation discontinuity, or erroneous edge delineation. label-efficient solutions for airway extraction are rarely explored yet primarily demanding in medical practice. To this end, we introduce a novel skeleton-level annotation (SkA) tailored to the airway, which simplifies the annotation workflow while enhancing annotation consistency and accuracy, preserving the complete topology. Furthermore, we propose a skeleton-supervised learning framework to achieve accurate airway segmentation. Firstly, a dual-stream buffer inference is introduced to realize initial label propagation from SkA, avoiding the collapse of direct learning from SkA. Then, we construct a geometry-aware dual-path propagation framework (GDP) to further promote complementary propagation learning, composed of hard geometry-aware propagation learning and soft geometry-aware propagation guidance. Experiments reveal that our proposed framework outperforms the competing methods with SKA, which amounts to only 1.96% airways, and achieves comparable performance with the baseline model that is fully supervised with 100% airways, demonstrating its significant potential in achieving label-efficient segmentation for other tubular structures, such as vessels

Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity

This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity. A novel Robust Average Gradient Algorithm (RAGA) is proposed, which leverages the geometric median for aggregation and can freely select the round number for local updating. Different from most existing resilient approaches, which perform convergence analysis based on strongly-convex loss function or homogeneously distributed dataset, we conduct convergence analysis for not only strongly-convex but also non-convex loss function over heterogeneous dataset. According to our theoretical analysis, as long as the fraction of dataset from malicious users is less than half, RAGA can achieve convergence at rate $\mathcal{O}({1}/{T^{2/3- \delta}})$ where $T$ is the iteration number and $\delta \in (0, 2/3)$ for non-convex loss function, and at linear rate for strongly-convex loss function. Moreover, stationary point or global optimal solution is proved to obtainable as data heterogeneity vanishes. Experimental results corroborate the robustness of RAGA to Byzantine attacks and verifies the advantage of RAGA over baselines on convergence performance under various intensity of Byzantine attacks, for heterogeneous dataset

Shared decision-making implementation status among dermatologists engaging in medical esthetics: a cross-sectional study in China

ObjectiveShared decision-making (SDM) is a collaborative process in which patients and healthcare providers jointly make a medical decision. This cross-sectional study aimed to identify the implementation status of shared decision-making among dermatologists engaging in medical esthetics in China and to identify factors associated with the good practice of SDM among them.MethodsFrom January to June 2023, a total of 1,287 dermatologists engaging in medical esthetics in China were recruited and completed the online interviews about their implementation of SDM based on the Shared Decision-Making Questionnaire for Doctors (SDM-Q-Doc). Logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) to explore factors associated with the higher SDM score achievement among dermatologists with medical esthetic practice.ResultsThe median value of the total SDM score was 39, and 48% (621/1278) of dermatologists with medical esthetic practice achieved at least 40 out of 45 scores. Logistic regression indicated that dermatologists aged 40–49 or ≥ 50 years and those engaging in medical esthetic practice for ≥5 years were more likely to achieve at least 40 out of 45 scores compared to dermatologists aged <30 years with less than 5 years of medical esthetic practice. The ORs were 1.82 (95% CI: 1.13–3.12), 1.94 (95% CI: 1.13–3.61), and 1.76 (95% CI: 1.34–2.31), respectively.ConclusionThe SDM implementation level among Chinese dermatologists engaging in medical esthetics is high, especially for those who are older age and have more years of practice. Hence, it is highly recommended to promote and enhance SDM practice among younger dermatologists engaging in medical esthetics with less working experience

Inherent SM Voltage Balance for Multilevel Circulant Modulation in Modular Multilevel DC--DC Converters

The modularity of a modular multilevel dc converter (MMDC) makes it attractive for medium-voltage distribution systems. Inherent balance of submodule (SM) capacitor voltages is considered as an ideal property, which avoids a complex sorting process based on many measurements thereby reducing costs and enhancing reliability. This article extends the inherent balance concept previously shown for square-wave modulation to a multilevel version for MMDCs. A switching duty matrix dU is introduced: it is a circulant matrix of preset multilevel switching patterns with multiple stages and multiple durations. Inherent voltage balance is ensured with a full-rank dU . Circulant matrix theory shows that this is equivalent to a simplified common factor criterion. A nonfull rank dU causes clusters of SM voltage rather than a single common value, with the clusters indicated by the kernel of the matrix. A generalized coprime criterion is developed into several deductions that serve as practical guidance for design of multilevel circulant modulation. The theoretical development is verified through full-scale simulations and downscaled experiments. The effectiveness of the proposed circulant modulation in achieving SM voltage balance in an MMDC is demonstrated

Simultaneous separation and quantitative determination of monosaccharides, uronic acids, and aldonic acids by high performance anion-exchange chromatography coupled with pulsed amperometric detection in corn stover prehydrolysates

A method for simultaneous separation and quantitative determination of arabinose, galactose, glucose, xylose, xylonic acid, gluconic acid, galacturonic acid, and glucuronic acid was developed by using high performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD). The separation was performed on a CarboPacTM PA-10 column (250 mm × 2 mm) with a various gradient elution of NaOH-NaOAc solution as the mobile phase. The calibration curves showed good linearity (R2 ≥ 0.9993) for the monosaccharides, uronic acids, and aldonic acids in the range of 0.1 to 12.5 mg/L. The detection limits (LODs) and the quantification limits (LOQs) were 4.91 to 18.75 μg/L and 16.36 to 62.50 μg/L, respectively. Relative standard deviations (RSDs) of the retention times and peak areas for the seven consecutive determinations of an unknown amount of mixture were 0.15% to 0.44% and 0.22% to 2.31%, respectively. The established method was used to separate and determine four monosaccharides, two uronic acids, and two aldonic acids in the prehydrolysate from dilute acid steam-exploded corn stover within 21 min. The spiked recoveries of monosaccharides, uronic acids, and aldonic acids ranged from 91.25% to 108.81%, with RSDs (n=3) of 0.04% ~ 6.07%. This method was applied to evaluate the quantitative variation of sugar and sugar acid content in biomass prehydrolysates

Do Stronger Patents Stimulate or Stifle Innovation? The Crucial Role of Financial Development

This study explores the effects of patent protection in a research and development (R&D)‐based growth model with financial frictions. We find that whether stronger patent protection stimulates or stifles innovation depends on credit constraints faced by R&D entrepreneurs. When credit constraints are nonbinding (binding), strengthening patent protection stimulates (stifles) R&D. The overall effect of patent protection on innovation follows an inverted‐U pattern. By relaxing the credit constraints, financial development stimulates innovation. Furthermore, patent protection is more likely to have a positive effect on innovation under a higher level of financial development. We consider cross‐country panel regressions and find supportive evidence for this result

Do Stronger Patents Stimulate or Stifle Innovation? The Crucial Role of Financial Development

This study explores the effects of patent protection in a distance-to-frontier R&D-based growth model with financial frictions. We find that whether stronger patent protection stimulates or stifles innovation depends on credit constraints faced by R&D entrepreneurs. When credit constraints are non-binding (binding), strengthening patent protection stimulates (stifles) R&D. The overall effect of patent protection on innovation follows an inverted-U pattern. An excessively high level of patent protection prevents a country from converging to the world technology frontier. A higher level of financial development influences credit constraints through two channels: decreasing the interest-rate spread and increasing the default cost. Via the interest-spread (default-cost) channel, patent protection is more likely to have a negative (positive) effect on innovation under a higher level of financial development. We test these results using cross-country regressions and find supportive evidence for the interest-spread channel