How Decentralized Federated Learning Will Work on W3AI
Decentralized Federated Learning (DFL) is a deep learning technique that has gained increasing adoption thanks to its privacy-preserving nature and improved efficiency compared to Centralized Federated Learning (CFL).
This article briefly introduces the concept of Federated Learning, analyzes why DFL is superior to CFL and offers an in-depth explanation of how DFL will work on the upcoming AIOZ W3AI.
Let's dive in:
The traditional AI model training method involves gathering data from various sources to form large datasets for training AI models on a single device/server belonging to a centralized entity.
While this method is pretty straightforward, it poses a serious threat to the data privacy of individuals whose data are AI training since a 3rd party has direct access to their data.
This issue has led to the rise of an innovative training method known as "Federated Learning" that enables AI model training on devices belonging to the owners of the training data.
The Federated Learning process usually starts by distributing an AI model across all the edge devices containing local data that will be used for training the model.
After training is completed across all the devices, every device uploads its trained version of the AI model to a central server that aggregates different model versions into a final version of the model.
This process of aggregating different models on a central server is known as Centralized Federated Learning (CFL), and it is currently the most adopted Federated Learning approach in AI development.
However, CFL still suffers a couple of challenges, such as mistrust of the entity handling the central server and the server's vulnerability to malicious attacks.
Decentralized Federated Learning (DFL) addresses these challenges by enabling edge devices to communicate and share model parameters with one another directly, eliminating the need for aggregation on a central server and enhancing privacy/security.
Let's see how DFL will work within the W3AI ecosystem:
AIOZ Web3 AI (W3AI) is a decentralized AI-as-a-service infrastructure powered by 180,000+ global edge nodes in the AIOZ DePIN and a Web3-incentivized collaborative AI marketplace.
W3AI will leverage both DFL and privacy-preserving techniques, such as Homomorphic Encryption (HE), to train and improve AI models in a collaborative manner.
The in-built HE capabilities of AIOZ DePIN nodes enable them to securely encrypt AI training data and model containers stored locally, with a local switching key created for decryption purposes.
The DFL process on AIOZ W3AI will start with the W3AI Task Manager using network topology to dispatch tasks and assign encrypted data/model containers to AIOZ DePIN nodes that meet the hardware requirements for the process.
This network topology can disconnect or reconnect connections, ensuring optimal process speed or readiness of computed results, if necessary.
During training, the selected nodes will utilize local data to execute AI model training tasks across several rounds while communicating with one another and sharing model parameters in the process.
W3AI will employ a Decentralized Periodic Averaging Stochastic Gradient Descent (DPASGD) to effectively update the weight of each participating node after every training round.
The performance of participating nodes will also be monitored by the W3AI Task manager, and $AIOZ token rewards will be distributed based on their contribution in each communication round.
This DFL computing workflow on AIOZ W3AI represents an intricately crafted system created with precision to tackle the urgent issues related to maintaining privacy in AI computations.
Such an innovative system enables the collaborative training of AI models across AIOZ DePIN while preserving data privacy and security, fostering a continuous cycle of Web3 AI advancement.
If you would like to learn more about AIOZ W3AI ahead of its upcoming release, you can download its vision paper in the link below:
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