Federated AI for building AI Solutions across Multiple Agencies

The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.

• Publication year

  2018

• Venue

  arXiv.org

• Publication date

  2018-09-20

• Fields of study

  Computer Science, Political Science

• Identifiers
  arXiv 1809.10036
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning

  2018cited by this paper
• An algorithm for model fusion for distributed learning

  2018cited by this paper
• Dynamic network based learning systems for sensor information fusion

  2017cited by this paper
• Communication-Efficient Learning of Deep Networks from Decentralized Data

  2016cited by this paper
• A Survey of Merging Decision Trees Data Mining Approaches

  2015cited by this paper
• Ensemble-based classifiers

  2010cited by this paper
• A Survey of Utility-based Privacy-Preserving Data Transformation Methods

  2008cited by this paper
• Policy Technologies for Self-Managing Systems

  2008cited by this paper
• A Fourier spectrum-based approach to represent decision trees for mining data streams in mobile environments

  2004cited by this paper
• Tree Induction vs. Logistic Regression: A Learning-Curve Analysis

  2003cited by this paper
• Learning Curves: Asymptotic Values and Rate of Convergence

  1993cited by this paper

• Incentive Mechanism Design for Hierarchical Federated Learning With Selfishness Queue Stability

  2025cites this paper
• An effective and efficient green federated learning method for one-layer neural networks

  2023cites this paper
• Accelerating Federated Learning With Cluster Construction and Hierarchical Aggregation

  2023cites this paper
• Federated Learning Over Wireless Networks: Challenges and Solutions

  2023cites this paper
• A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

  2023cites this paper
• A pipeline to collaborative AI models creation between Brazilian governmental institutions

  2023cites this paper
• APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning

  2023cites this paper
• Enhanced Federated Learning with Adaptive Block-wise Regularization and Knowledge Distillation

  2023cites this paper
• Federated Learning and Meta Learning: Approaches, Applications, and Directions

  2022cites this paper
• Enhancing Federated Learning with In-Cloud Unlabeled Data

  2022cites this paper
• Federated and Meta learning over Non-Wireless and Wireless Networks: A Tutorial

  2022cites this paper
• Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing

  2021cites this paper
• Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

  2021cites this paper
• Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression

  2021cites this paper
• Inter-Server Collaborative Federated Learning for Ultra-Dense Edge Computing

  2021cites this paper
• Big Data Analytics Framework for Predictive Analytics using Public Data with Privacy Preserving

  2021cites this paper
• ClusterGrad: Adaptive Gradient Compression by Clustering in Federated Learning

  2020cites this paper
• Beyond privacy regulations: an ethical approach to data usage in transportation

  2020cites this paper
• Simulating Aggregation Algorithms for Empirical Verification of Resilient and Adaptive Federated Learning

  2020cites this paper
• Federated Learning for Coalition Operations

  2019cites this paper
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey

  2019cites this paper
• Federated AI for the Enterprise: A Web Services Based Implementation

  2019influential citation
• EASE: Energy‐efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

  2019cites this paper
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

  2019cites this paper
• A conceptual architecture for contractual data sharing in a decentralised environment

  2019cites this paper
• Policy-Based Autonomic Data Governance

  2019cites this paper
• Generative Policies for Coalition Systems - A Symbolic Learning Framework

  2019cites this paper
• Approaches to address the data skew problem in federated learning

  2019cites this paper
• Towards Enabling Trusted Artificial Intelligence via Blockchain

  2018cites this paper
• Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption

  2018cites this paper