+ Draft Community Group Report + +
++ Copyright + © + 2022-2023 + + the Contributors to the Federated Learning API + Specification, published by the + Federated Learning Community Group under the + W3C Community Final Specification Agreement (FSA). A human-readable + summary + is available. + +
+This proposal defines interfaces that enables the implementation and management of federated learning systems.
++ This specification was published by the + Federated Learning Community Group. It is not a W3C Standard nor is it + on the W3C Standards Track. + + Please note that under the + W3C Community Final Specification Agreement (FSA) + other conditions apply. + + Learn more about + W3C Community and Business Groups. +
+ GitHub Issues are preferred for + discussion of this specification. + + +
+The scope of the Federated Learning API specification encompasses the development of a standardized interface that enables the implementation and management of federated learning systems. + It focuses on the communication and coordination between central servers and client devices participating in federated learning, with an emphasis on privacy-preserving machine learning techniques. + The specification covers the interactions, protocols, and data formats necessary for secure and efficient model training across decentralized devices.
+ +Need to be developed.
+As well as sections marked as non-normative, all authoring guidelines, diagrams, examples, and notes in this specification are non-normative. Everything else in this specification is normative.
+ The key word MUST in this document + is to be interpreted as described in + BCP 14 + [RFC2119] [RFC8174] + when, and only when, they appear in all capitals, as shown here. +
The functional requirements for Federated Learning API outline the essential capabilities and specifications necessary to enable seamless communication, + secure data transmission, and effective coordination of federated learning systems. These requirements encompass device registration, data upload, model synchronization, + evaluation, result retrieval, training control, and ensuring security and privacy measures.
+ +The API should allow devices or clients to securely register themselves with the federated learning system. + The API should allow devices or clients to securely register themselves with the federated learning system. + The API should provide endpoints for device registration, including necessary parameters and data formats.
+ +The API should enable clients to securely upload their locally held data to the central server or coordinator for model training. + It should support various data formats and provide guidelines for data serialization and transmission. + The API should include endpoints for data submission, metadata specification, and possibly data encryption or anonymization.
+ +The API should handle the communication and synchronization of model parameters between the central server and client devices. + It should provide endpoints for retrieving the current model state, sending model updates from clients to the server, and distributing updated models back to the clients. + The API should support efficient and secure transmission of model parameters, taking into account bandwidth limitations and data privacy requirements.
+ +The API should include endpoints for clients to request model evaluation on their local data. + It should support the transmission of evaluation requests and relevant data securely to the server. + The API should allow clients to retrieve evaluation metrics or results from the server.
+ +The API should enable clients to retrieve the final trained model or other relevant results from the central server. + It should provide endpoints for requesting and downloading the trained model or aggregated results. + The API should ensure secure transmission of results and provide mechanisms for access control to protect sensitive information.
+ +The API should allow for controlling the federated learning process, such as starting, pausing, or terminating model training. + It should provide endpoints for managing training sessions, setting training parameters, and monitoring the progress of training. + The API should support error handling and provide appropriate status codes for different training control operations.
+ +The API should incorporate security measures, such as authentication, encryption, and access control, to protect the federated learning system. + It should ensure the privacy and confidentiality of data during transmission and storage. + The API should adhere to privacy regulations and best practices for handling sensitive information.
+ +The API should be designed with extensibility in mind, allowing for the addition of new functionalities or endpoints in the future. + It should be compatible with existing web standards and frameworks, facilitating integration with different software platforms and tools.
+ +The methodologies section outlines the fundamental techniques and approaches to federated learning. + This section encompasses neural network parameter aggregation strategies, model training and optimization, synchronous and asynchronous learning, and parameter server operations.
+ +The foundational algorithms for federated learning, Federated Averaging, or FedAvg, aggregates model updates by averaging them. + The central server calculates a weighted average based on the number of data points from each client.
+ +For model training, clients can use their local data to train models. The frequency of synchronization with the central server varies based on + the algorithm and design decisions. To further ensure data privacy, noise can be added to model updates, providing differential privacy. + This protects individual data points while allowing the central model to learn general patterns.
+ +Depending on the application, federated learning can operate in synchronous mode (all clients send updates simultaneously) or asynchronous mode (clients send updates at different times). + The bandwidth optimization techniques such as model compression or quantization can be used to reduce the size of model updates, optimizing for limited bandwidth scenarios.
+ +A hierarchical structure can be used where multiple local aggregators collect updates before sending to the central server for large-scale deployments. Techniques and tools to optimize + parameter server, such as model pruning of neural network, can be supported to make federated learning more optimalize for resource-constrained devices. + +
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