FEDML’s Recent Advances in Federated Learning (2023-2024)

FEDML’s Recent Advances in Federated Learning (2023-2024)

As a pioneer in the field of federated learning, FEDML initially focused on an AI platform dedicated to federated learning. Over time, it evolved into a comprehensive "Generative AI Platform at Scale". While making this transformation, we still kept making strong progress and achieving significant milestones in the federated learning domain. In this post, we'll reflect on our perspectives regarding federated learning within the Generative AI (GenAI) landscape and recap the strides we've made over the previous year.

Mission and Vision of FEDML in Federated Learning

At FEDML, we believe that in the era of Generative AI, federated learning will play a pivotal role. It can fundamentally impact the way large language models (LLMs) and other Generative AI models are trained and deployed, particularly to address critical bottlenecks related to computational resources, data access, and privacy. By enabling the distribution of LLM training and deployment across a decentralized cloud infrastructure, federated learning significantly alleviates the GPU and compute limitations that have traditionally constrained the scalability of AI systems. 

Furthermore, federated learning is instrumental in overcoming the data bottleneck, a prevalent challenge as we scale the size of GenAI models. It achieves this by facilitating access to a vast array of data points located at the edge of the network—such as smartphones, personal computers, and IoT devices—in a manner that prioritizes privacy and security. This approach enables the integration of diverse and rich datasets into the training process, substantially enriching the learning capabilities of AI models while safeguarding user privacy.

Finally, federated learning plays a crucial role in transitioning LLMs and Generative AI technologies from centralized cloud setups to the edge, leading to more rapid deployment, reduced operational costs, and improved personalization and privacy. By processing data locally on edge devices, federated learning minimizes dependency on constant cloud connectivity, leading to faster, cheaper, more reliable, and private AI solutions.

Highlights of Our Recent Advanced in Federated Learning

1. The most widely used federated learning platform: 5000 platform users, 4K+ GitHub Stars, 500+ citations

Platform: https://fedml.ai

GitHub: https://github.com/FedML-AI/FedML

2. The first and only FLOps (MLOps for FL) platform publicly available in the market

User Guide: https://fedml.ai/federate/octopus/userGuides

Docs: https://doc.fedml.ai/federate

3. FEDML library is Upgraded to Generative AI library for Generic Training, Deployment, AI Job Super Launcher across GPU Clouds

4. Pioneer in FedLLM: Released the first Federated Learning library for Large Language Model in April 2023

Blog: https://fedml.medium.com/releasing-fedllm-build-your-own-large-language-models-on-proprietary-data-using-the-fedml-platform-ec9e10bda04b

5. Integrated Federated Learning Service in FEDML Nexus AI

Job Store: https://fedml.ai/job-store/federate

6. Run Serverless Simulation and then Scale in Production


About FEDML, Inc.

FEDML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FEDML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy.