Adapting Federated Learning for Heterogeneous Environments: Mitigating Negative Knowledge Transfer with Cohort-Based Clustering

CommunityAI is a novel framework for federated learning (FL) that enables decentralized collaboration among communities while ensuring data privacy and security. By introducing a metadata model and sharing protocol, CommunityAI provides a standardized way for communities to describe themselves and their collaboration preferences. This allows for secure and selective exchange of metadata between communities, enabling FL tasks to be performed in a decentralized manner. The proposed architecture and processes provide a comprehensive solution for community-based FL, making it more accessible and scalable than traditional centralized approaches.

Introduction

In the era of big data, federated learning (FL) has emerged as a promising technology that enables multiple parties to collaboratively train machine learning models on their collective data without compromising data privacy or security. However, most FL frameworks rely on centralized structures, which can limit their scalability and flexibility. CommunityAI addresses these limitations by introducing a decentralized framework for FL that leverages community-based collaboration.

Metadata Model and Sharing Protocol

At the core of CommunityAI lies a metadata model that enables communities to describe themselves and their collaboration preferences in a standardized way. This model includes information about the types of data a client has, its expertise, its willingness to collaborate, or any specific criteria it requires for participating in FL tasks. The metadata is represented in a structured format, such as attributes and semantics, which facilitates secure and selective exchange between communities. The sharing protocol defines the rules and procedures for exchanging metadata, ensuring that data remains private and secure throughout the collaboration process.

Decentralized Federation

CommunityAI enables decentralized FL by allowing communities to collaborate without relying on a central authority. Each community maintains control over its own data and makes decisions about how to contribute to the overall model. This approach allows for greater flexibility in terms of data sharing, as communities can choose to collaborate based on their own criteria rather than being dictated by a centralized structure. The decentralized federation enables faster and more scalable collaboration, making it possible for larger numbers of communities to participate in FL tasks.

Architecture and Processes

The proposed architecture for CommunityAI consists of several components that work together to enable community-based FL. These include:

1. Community Manager: This component is responsible for managing the overall federation, including onboarding new communities and maintaining communication channels between them.
2. Metadata Manager: This component is responsible for storing and sharing metadata between communities, ensuring that data remains private and secure throughout the collaboration process.
3. FL Engine: This component is responsible for executing FL tasks across multiple communities, leveraging the shared metadata to train machine learning models without compromising data privacy or security.
4. Security Mechanisms: These mechanisms are responsible for ensuring the integrity and confidentiality of data throughout the collaboration process, using techniques such as encryption, secure communication channels, and access control.

The proposed processes for CommunityAI include

1. Onboarding Communities: This process involves onboarding new communities into the federation, including configuring their metadata and setting up communication channels.
2. Sharing Metadata: This process involves exchanging metadata between communities, ensuring that data remains private and secure throughout the collaboration process.
3. Executing FL Tasks: This process involves executing FL tasks across multiple communities, leveraging the shared metadata to train machine learning models without compromising data privacy or security.

Conclusion

In summary, CommunityAI is a decentralized framework for federated learning that enables community-based collaboration while ensuring data privacy and security. By introducing a metadata model and sharing protocol, CommunityAI provides a standardized way for communities to describe themselves and their collaboration preferences. This allows for secure and selective exchange of metadata between communities, enabling FL tasks to be performed in a decentralized manner. The proposed architecture and processes provide a comprehensive solution for community-based FL, making it more accessible and scalable than traditional centralized approaches.