130 research outputs found
Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks
This paper reintroduces the notion of resilience in the context of recent
issues originated from climate change triggered events including severe
hurricanes and wildfires. A recent example is PG&E's forced power outage to
contain wildfire risk which led to widespread power disruption. This paper
focuses on answering two questions: who is responsible for resilience? and how
to quantify the monetary value of resilience? To this end, we first provide
preliminary definitions of resilience for power systems. We then investigate
the role of natural hazards, especially wildfire, on power system resilience.
Finally, we will propose a decentralized strategy for a resilient management
system using distributed storage and demand response resources. Our proposed
high fidelity model provides utilities, operators, and policymakers with a
clearer picture for strategic decision making and preventive decisions
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Federated learning (FL) is gaining increasing popularity in the medical
domain for analyzing medical images, which is considered an effective technique
to safeguard sensitive patient data and comply with privacy regulations.
However, several recent studies have revealed that the default settings of FL
may leak private training data under privacy attacks. Thus, it is still unclear
whether and to what extent such privacy risks of FL exist in the medical
domain, and if so, "how to mitigate such risks?". In this paper, first, we
propose a holistic framework for Medical data Privacy risk analysis and
mitigation in Federated Learning (MedPFL) to analyze privacy risks and develop
effective mitigation strategies in FL for protecting private medical data.
Second, we demonstrate the substantial privacy risks of using FL to process
medical images, where adversaries can easily perform privacy attacks to
reconstruct private medical images accurately. Third, we show that the defense
approach of adding random noises may not always work effectively to protect
medical images against privacy attacks in FL, which poses unique and pressing
challenges associated with medical data for privacy protection.Comment: V
Sparsity-Based Error Detection in DC Power Flow State Estimation
This paper presents a new approach for identifying the measurement error in
the DC power flow state estimation problem. The proposed algorithm exploits the
singularity of the impedance matrix and the sparsity of the error vector by
posing the DC power flow problem as a sparse vector recovery problem that
leverages the structure of the power system and uses -norm minimization
for state estimation. This approach can provably compute the measurement errors
exactly, and its performance is robust to the arbitrary magnitudes of the
measurement errors. Hence, the proposed approach can detect the noisy elements
if the measurements are contaminated with additive white Gaussian noise plus
sparse noise with large magnitude. The effectiveness of the proposed
sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE
118-bus and 300-bus test systems
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing
Federated Learning (FL) has gained widespread popularity in recent years due
to the fast booming of advanced machine learning and artificial intelligence
along with emerging security and privacy threats. FL enables efficient model
generation from local data storage of the edge devices without revealing the
sensitive data to any entities. While this paradigm partly mitigates the
privacy issues of users' sensitive data, the performance of the FL process can
be threatened and reached a bottleneck due to the growing cyber threats and
privacy violation techniques. To expedite the proliferation of FL process, the
integration of blockchain for FL environments has drawn prolific attention from
the people of academia and industry. Blockchain has the potential to prevent
security and privacy threats with its decentralization, immutability,
consensus, and transparency characteristic. However, if the blockchain
mechanism requires costly computational resources, then the
resource-constrained FL clients cannot be involved in the training. Considering
that, this survey focuses on reviewing the challenges, solutions, and future
directions for the successful deployment of blockchain in resource-constrained
FL environments. We comprehensively review variant blockchain mechanisms that
are suitable for FL process and discuss their trade-offs for a limited resource
budget. Further, we extensively analyze the cyber threats that could be
observed in a resource-constrained FL environment, and how blockchain can play
a key role to block those cyber attacks. To this end, we highlight some
potential solutions towards the coupling of blockchain and federated learning
that can offer high levels of reliability, data privacy, and distributed
computing performance
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