Federated Learning (FL) trains models using distributed data. Differential Privacy (DP) provides privacy guarantees. The goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, DP-noise increases as model size grows, hindering convergence. Partial Embedding Updates (PEU) is proposed to decrease noise by reducing payload size.
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Practical AI Solutions for Middle Managers
Federated Learning and Differential Privacy
Federated Learning (FL) is a technique that allows training AI models using data from multiple devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while maintaining privacy using FL and DP. However, the noise introduced by DP increases as the model size grows, hindering convergence. We propose Partial Embedding Updates (PEU) as a novel technique to reduce noise by decreasing payload size.
AI Implementation Tips
If you want to leverage AI to stay competitive and evolve your company, consider Training Large-Vocabulary Neural Language Model by Private Federated Learning for Resource-Constrained Devices. Here are some practical steps to implement AI effectively:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI initiatives have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and allow for customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
Connect with Us
For AI KPI management advice, reach out to us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram or Twitter.
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