185 research outputs found
MMZDA: Enabling Social Welfare Maximization in Cross-Silo Federated Learning
—As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this social dilemma, we employ the Multi-player Multi-action ZeroDeterminant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Since the MMZDstrategy can be adopted by all organizations, we further study the scenario where multiple organizations jointly adopt the MMZD strategy and form an MMZD Alliance (MMZDA). We prove theoretically that the MMZDA strategy strengthens the control of the maximum social welfare. Experimental results validate that the MMZD strategy is effective in maximizing the social welfare and the MMZDA can achieve a larger maximum value
Strategic signaling for utility control in audit games
As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit game to warn visitors can improve the benefits of the defender. However, the previous approaches usually assume perfect information of the attacker, or simply concentrate on the utility of the defender. In this paper, we introduce a brand-new zero-determinant (ZD) strategy to study the sequential audit game with online signaling, which empowers the defender to unilaterally control the utility of visitors when accessing sensitive data. In addition, an optimization scheme based on the ZD strategy is designed to effectively maximize the utility difference between the defender and the attacker. Extensive simulation results show that our proposed scheme enhances the security management and control capabilities of the defender to better handle different access requests and safeguard the system security in a cost-efficient manner
Dynamic Loss For Robust Learning
Label noise and class imbalance commonly coexist in real-world data. Previous
works for robust learning, however, usually address either one type of the data
biases and underperform when facing them both. To mitigate this gap, this work
presents a novel meta-learning based dynamic loss that automatically adjusts
the objective functions with the training process to robustly learn a
classifier from long-tailed noisy data. Concretely, our dynamic loss comprises
a label corrector and a margin generator, which respectively correct noisy
labels and generate additive per-class classification margins by perceiving the
underlying data distribution as well as the learning state of the classifier.
Equipped with a new hierarchical sampling strategy that enriches a small amount
of unbiased metadata with diverse and hard samples, the two components in the
dynamic loss are optimized jointly through meta-learning and cultivate the
classifier to well adapt to clean and balanced test data. Extensive experiments
show our method achieves state-of-the-art accuracy on multiple real-world and
synthetic datasets with various types of data biases, including CIFAR-10/100,
Animal-10N, ImageNet-LT, and Webvision. Code will soon be publicly available
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning
Traffic flow prediction is an important part of smart transportation. The
goal is to predict future traffic conditions based on historical data recorded
by sensors and the traffic network. As the city continues to build, parts of
the transportation network will be added or modified. How to accurately predict
expanding and evolving long-term streaming networks is of great significance.
To this end, we propose a new simulation-based criterion that considers
teaching autonomous agents to mimic sensor patterns, planning their next visit
based on the sensor's profile (e.g., traffic, speed, occupancy). The data
recorded by the sensor is most accurate when the agent can perfectly simulate
the sensor's activity pattern. We propose to formulate the problem as a
continuous reinforcement learning task, where the agent is the next flow value
predictor, the action is the next time-series flow value in the sensor, and the
environment state is a dynamically fused representation of the sensor and
transportation network. Actions taken by the agent change the environment,
which in turn forces the agent's mode to update, while the agent further
explores changes in the dynamic traffic network, which helps the agent predict
its next visit more accurately. Therefore, we develop a strategy in which
sensors and traffic networks update each other and incorporate temporal context
to quantify state representations evolving over time
Domain Adaptation with Incomplete Target Domains
Domain adaptation, as a task of reducing the annotation cost in a target
domain by exploiting the existing labeled data in an auxiliary source domain,
has received a lot of attention in the research community. However, the
standard domain adaptation has assumed perfectly observed data in both domains,
while in real world applications the existence of missing data can be
prevalent. In this paper, we tackle a more challenging domain adaptation
scenario where one has an incomplete target domain with partially observed
data. We propose an Incomplete Data Imputation based Adversarial Network
(IDIAN) model to address this new domain adaptation challenge. In the proposed
model, we design a data imputation module to fill the missing feature values
based on the partial observations in the target domain, while aligning the two
domains via deep adversarial adaption. We conduct experiments on both
cross-domain benchmark tasks and a real world adaptation task with imperfect
target domains. The experimental results demonstrate the effectiveness of the
proposed method
China’s Conversion of Cropland to Forest Program as a national PES scheme : Institutional structure, voluntarism and conditionality of PES
Forest Landscape Restoration in Hilly and Mountainous Regions: Special Issue: The ‘Conversion of Cropland to Forest Program’ (CCFP) as a national ‘Payment for Ecosystem Services’ (PES) scheme in China: Institutional structure and roles, ensuring voluntarism and conditionality of subsidy paymentsChina’s ‘Conversion of Cropland to Forest Program’ (CCFP) is one of the world’s largest national ‘Payment for Ecosystem Services’ (PES) programs, with over 32 million rural households enrolled and 28 million ha converted to forest since 1999. Given the scale of the program and emerging interest in forest landscape restoration, the structure and function of implementation models is of interest. This study is based on key informant interviews tracing the structure and interactions among institutions for implementation of the CCFP from central government to provincial and sub-provincial scales in Yunnan Province. Data are used to analyze implementation arrangements for program planning, implementation and monitoring, and to identify features ensuring conditionality of PES payments. To assess the degree of voluntarism in enrollment, the study employs data from 87 household-level interviews in four southwestern provinces. Findings indicate that the CCFP system is designed to fulfill expectations of PES programs in terms of conditionality and voluntary participation on the side of ecosystem service sellers.Peer reviewe
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