Overcoming Forgetting, in Federated Learning on Non-IID Data.
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning.
Overcoming Forgetting in Federated Learning on Non-IID Data.
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning.
Distributed training on edge devices: Batch Normalisation.
An Edgify Research Team Publication. In the first post of this series, we presented two basic approaches to distributed…
Distributed Training on Edge Devices. Large Batch vs. Federated Learning.
An Edgify Research Team Publication, this is the first, introductory post, in our three-part algorithmic series on real-world distributed training.
Distributed Training on Edge Devices: Communication compression.
An Edgify.ai Research Team Publication. In the first post of this series, we presented two basic approaches to distributed training on edge…
Combatting Retail Shrink While Preserving Self-Checkout CX
Retail shrink, including loss at the self-checkout, has reached colossal proportions. The Wall Street Journal reports that U.S. retailers lost an estimated $142 billion in 2023, a 25% increase from the previous year.
Bringing AI to Retail’s Edge
From unreadable barcodes to mislabelled products, even small identification errors can have a big impact on retailers. Discover how edge AI is transforming product recognition at checkout, reducing shrinkage, improving inventory accuracy, and delivering a faster, more reliable customer experience.
Revolutionizing AI with Edge-Based MLOps: The Future of Cost-Effective and Privacy-Preserving Solutions.
In recent years, the field of artificial intelligence (AI) has made remarkable strides, transforming industries and reshaping the way businesses operate.