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This lesson delves into the concept of protection and deprotection of a functional group fundamental to synthetic organic chemistry. These phenomena are explained in the context of aliphatic and aromatic alcohols.
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Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Related Experiment Video

Updated: Feb 14, 2026

Procedure and Key Optimization Strategies for an Automated Capillary Electrophoretic-based Immunoassay Method
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Privacy Protection Optimization Method for Cloud Platforms Based on Federated Learning and Homomorphic Encryption.

Jing Wang1, Yun Wang1

  • 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, enhancing distributed training security and efficiency. HFHE-Cloud achieves high privacy protection and computing performance in multi-tenant cloud environments.

Keywords:
cloud computingdistributed trainingfederal studyhomomorphic encryptionprivacy protection

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Area of Science:

  • Cloud Computing
  • Distributed Systems
  • Cryptography
  • Machine Learning

Background:

  • Distributed training in cloud environments faces challenges like privacy leakage, communication redundancy, and reduced aggregation efficiency due to multi-tenancy and high concurrency.
  • Existing federated learning models struggle to balance privacy protection with computational performance in complex cloud infrastructures.

Purpose of the Study:

  • To develop a secure and efficient collaborative learning framework for cloud platforms that optimizes privacy protection and computing performance.
  • To address the performance bottlenecks associated with encryption calculations and communication delays in distributed training.

Main Methods:

  • Proposed the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, integrating federated learning (FL) with homomorphic encryption.
  • Implemented a hierarchical key mapping and dynamic scheduling mechanism for heterogeneous nodes to reduce performance bottlenecks.
  • Ensured data privacy by not exposing original data during the collaborative training process.

Main Results:

  • HFHE-Cloud significantly outperformed baseline models (FedAvg, FedProx, FedPer, FedNova, HE-FedAvg) in comprehensive performance.
  • Achieved high privacy protection with global accuracy up to 94.25% and stable loss within 0.09.
  • Improved computing performance by reducing encryption/decryption time by approximately one-third, with a 13% encryption overhead.
  • Enhanced distributed training efficiency by reducing communication rounds by about one-fifth and maintaining over 90% node participation.

Conclusions:

  • The HFHE-Cloud model demonstrates high security and scalability, suitable for multi-tenant cloud environments.
  • Provides a viable technical solution for cloud service providers and enterprises seeking robust privacy protection and efficient collaborative training.
  • Successfully balances intensive privacy requirements with efficient distributed learning performance on cloud platforms.