FedML
| Entity Passport | |
| Registry ID | gh-tool--fedml-ai--fedml |
| License | Apache-2.0 |
| Provider | github |
Cite this model
Academic & Research Attribution
@misc{gh_tool__fedml_ai__fedml,
author = {Fedml Ai},
title = {FedML Model},
year = {2026},
howpublished = {\url{https://github.com/fedml-ai/fedml}},
note = {Accessed via Free2AITools Knowledge Fortress}
} 🔬Technical Deep Dive
Full Specifications [+]▾
Quick Commands
git clone https://github.com/fedml-ai/fedml ⚖️ Nexus Index V2.0
💬 Index Insight
FNI V2.0 for FedML: Semantic (S:50), Authority (A:0), Popularity (P:67), Recency (R:69), Quality (Q:70).
Verification Authority
🚀 What's Next?
Technical Deep Dive
FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale
Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)
TensorOpera Documentation: https://docs.TensorOpera.ai
TensorOpera Homepage: https://TensorOpera.ai/
TensorOpera Blog: https://blog.TensorOpera.ai/
Join the Community:
Slack: https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w
Discord: https://discord.gg/9xkW8ae6RV
TensorOpera® AI (https://TensorOpera.ai) is the next-gen cloud service for LLMs & Generative AI. It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely.
Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds.
A typical workflow is showing in figure above. When developer wants to run a pre-built job in Studio or Job Store, TensorOpera®Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. When running the job, TensorOpera®Launch orchestrates the compute plane in different cluster topologies and configuration so that any complex AI jobs are enabled, regardless model training, deployment, or even federated learning. TensorOpera®Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale.
In the MLOps layer of TensorOpera AI
- TensorOpera® Studio embraces the power of Generative AI! Access popular open-source foundational models (e.g., LLMs), fine-tune them seamlessly with your specific data, and deploy them scalably and cost-effectively using the TensorOpera Launch on GPU marketplace.
- TensorOpera® Job Store maintains a list of pre-built jobs for training, deployment, and federated learning. Developers are encouraged to run directly with customize datasets or models on cheaper GPUs.
In the scheduler layer of TensorOpera AI
- TensorOpera® Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, serverless deployments, and vector DB searches. TensorOpera Launch also facilitates on-prem cluster management and deployment on private or hybrid clouds.
In the Compute layer of TensorOpera AI
- TensorOpera® Deploy is a model serving platform for high scalability and low latency.
- TensorOpera® Train focuses on distributed training of large and foundational models.
- TensorOpera® Federate is a federated learning platform backed by the most popular federated learning open-source library and the world’s first FLOps (federated learning Ops), offering on-device training on smartphones and cross-cloud GPU servers.
- TensorOpera® Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale.
Contributing
FedML embraces and thrive through open-source. We welcome all kinds of contributions from the community. Kudos to all of our amazing contributors!
FedML has adopted Contributor Covenant.
⚠️ Incomplete Data
Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.
View Original Source →📝 Limitations & Considerations
- • Benchmark scores may vary based on evaluation methodology and hardware configuration.
- • VRAM requirements are estimates; actual usage depends on quantization and batch size.
- • FNI scores are relative rankings and may change as new models are added.
- ⚠ License Unknown: Verify licensing terms before commercial use.
AI Summary: Based on GitHub metadata. Not a recommendation.
🛡️ Model Transparency Report
Technical metadata sourced from upstream repositories.
🆔 Identity & Source
- id
- gh-tool--fedml-ai--fedml
- slug
- fedml-ai--fedml
- source
- github
- author
- Fedml Ai
- license
- Apache-2.0
- tags
- federated-learning, deep-learning, distributed-training, edge-ai, machine-learning, on-device-training, inference-engine, mlops, model-deployment, model-serving, ai-agent, python
⚙️ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
- pipeline tag
- other
📊 Engagement & Metrics
- downloads
- 0
- stars
- 0
- forks
- 0
Data indexed from public sources. Updated daily.