Federated AI and the Fedwiki

gemini

**Federated AI**, which is a type of **Federated Learning (FL)**, is a machine learning technique that trains an Artificial Intelligence model across multiple decentralized edge devices or servers. This process is collaborative and distributed, with a key defining characteristic being that **raw data remains local** on the client devices, and only model updates (like learned weights or gradients) are sent to a central server for aggregation. --- ## What is Federated AI (Federated Learning)? 🧠 Federated AI fundamentally shifts the training paradigm from moving the data to a central server to **moving the computation (the model) to the data**. ### How it Works 1. **Initialization:** A central server creates and distributes an initial global AI model to selected client devices (nodes), such as mobile phones, IoT devices, or local organizational servers. 2. **Local Training:** Each client trains the model locally using its own private data. The client's raw data never leaves the device. 3. **Update Communication:** The clients send only the small, local **model updates** (the changes/improvements they learned) back to the central server, often using secure and encrypted communication. 4. **Aggregation:** The central server receives updates from many clients and uses an aggregation algorithm (like **Federated Averaging** or FedAvg) to combine these updates into a single, improved **global model**. 5. **Iteration:** The new global model is then redistributed to the clients, and the process repeats until the model reaches its desired performance. ### Key Advantages The primary motivations for using Federated AI are: * **Privacy and Security:** By keeping sensitive raw data on the local devices, it minimizes the risk of data exposure and helps organizations comply with stringent regulations like GDPR and HIPAA. * **Access to Siloed Data:** It allows AI models to be trained on diverse, real-world data sources that might otherwise be inaccessible due to regulatory, security, or proprietary restrictions (e.g., medical records, financial data). * **Efficiency:** Transmitting small model updates is often more efficient and less costly than transferring massive, raw datasets, especially in scenarios involving numerous edge devices with limited bandwidth. --- ## Relationship Between Federated AI and FedWiki While they share the word "federated," **Federated AI (or Federated Learning) and FedWiki are distinct technologies** that apply the concept of **decentralization and collaboration** to different domains. | Technology | Core Function | "Federation" Principle | Domain | | :--- | :--- | :--- | :--- | | **Federated AI** | Training a collective AI model without centralizing the training data. | The **AI model** is shared and collaboratively improved across distributed private data silos. | Machine Learning and Data Science. | | **FedWiki** (Federated Wiki) | A software architecture that allows for a decentralized collection of individual wiki sites. | **Knowledge creation and content** are distributed across many independent, yet connected, wiki sites (the "federation"). | Knowledge Management and Collaborative Writing. | ### The Common Ground: Decentralized Collaboration The conceptual link lies in their shared design philosophy: **distributed collaboration without full centralization**. * **FedWiki** decentralizes the **authority and version of knowledge**. Instead of one consensus version (like Wikipedia), each user or group maintains their own wiki, can "fork" (copy) others' pages to edit locally, and share connections and references back into the "federation." The collective knowledge emerges from these connected, individual viewpoints. * **Federated AI** decentralizes the **data and training process**. Instead of one central data repository, the knowledge (the AI model) is collectively built from many individual data sources without those sources ever having to combine their private data. The collective intelligence (the global model) emerges from aggregating the local updates. In essence, both systems embrace a "federated" structure to enable an improved, collective outcome—an AI model in one case, and a distributed knowledge commons in the other—while maintaining the autonomy and privacy of the individual nodes (devices/users/organizations).

deepseek

### What is Federated AI (Federated Learning)? Federated AI, more commonly known as **Federated Learning**, is a machine learning technique that allows you to train an AI model across multiple decentralized devices or servers holding local data samples, **without exchanging the data itself.** Think of it as a "bring the code to the data" model, as opposed to the traditional "bring the data to the code" model. **The Traditional Model vs. Federated Learning:** * **Traditional Centralized AI:** 1. All user data is collected and sent to a central server (e.g., in the cloud). 2. A single, powerful model is trained on this massive, centralized dataset. 3. **Problem:** This creates huge privacy risks, requires significant bandwidth, and makes you the product by handing over your data. * **Federated Learning:** 1. **Local Training:** A global model (e.g., for predicting the next word on your phone keyboard) is sent to your device. 2. **Learn on Your Device:** Your device uses its local data (your typing history) to improve the model *on the device itself*. Your personal data never leaves your phone. 3. **Share Only the Learnings:** Only the model *updates* (the learned weights and gradients, not the raw data) are sent back to the central server. 4. **Aggregate Updates:** The server collects updates from thousands or millions of devices and averages them together to improve the global model. 5. **Distribute Improved Model:** The improved global model is then sent back to all devices. **Key Benefits of Federated AI:** * **Privacy Preservation:** Raw data never leaves the user's device. * **Reduced Bandwidth:** Only small model updates are transmitted, not massive datasets. * **Personalization:** Models can learn from user-specific behavior while still benefiting from collective intelligence. --- ### What is the Fedwiki? The **Fedwiki** (Federated Wiki) is a concept and a specific software project pioneered by Ward Cunningham, the inventor of the original wiki. It reimagines the wiki not as a single, centralized website (like Wikipedia), but as a federation of individual, personal wikis. **The Traditional Wiki vs. The Federated Wiki:** * **Traditional Wiki (e.g., Wikipedia):** * A single, central server hosts all the pages. * All edits and contributions are made to that one, canonical version of the truth. * It's a **monolithic** structure. * **Federated Wiki:** * **Everyone has their own wiki.** You host your own site with your own pages. * **Forking is the primary action.** If you find a page on someone else's Fedwiki that you like, you "fork" it to your own wiki. It becomes a copy that you now control and can edit. * **Linking and Federation:** Your wiki can link to pages on other wikis. You can "follow" other wikis, and your wiki will show you updates from them. The content is **federated**—it's distributed across many sites but can be seamlessly viewed and connected. * It's a **networked** or **federated** structure. **Key Ideas of Fedwiki:** * **Ownership:** You own your content and your platform. * **Forking as Collaboration:** Instead of arguing over a single version of a page, you fork it and create your own perspective. The network of forks tells a story of evolving thought. * **Small Pieces, Loosely Joined:** The web of small, independent wikis creates a resilient and diverse ecosystem of knowledge. --- ### How Federated AI Relates to the Fedwiki The connection isn't about one technology directly using the other, but rather a shared **philosophical principle**: **Decentralization and moving computation to the edge.** Both concepts are reactions against the centralized, data-hoarding model of the modern web. Here are the core relational points: | Feature | Federated AI | Fedwiki | Shared Philosophy | | :--- | :--- | :--- | :--- | | **Architecture** | Decentralized model training across many devices. | Decentralized content creation across many servers. | **Rejects the central server as the sole authority.** | | **Data/Content** | Your data stays on your device. | Your content stays on your server. | **Empowers the individual and prioritizes ownership.** | | **Collaboration** | Devices collaborate by sharing model _updates_, not raw data. | Wikis collaborate by _forking_ and _linking_ pages, not editing a single source. | **Collaboration happens through the aggregation of individual contributions, not direct merging into a central repository.** | | **The "Federation"** | A federation of devices working together to train a model. | A federation of wikis working together to build knowledge. | **The whole is smarter and richer than the sum of its parts, without any single part giving up its autonomy.** | ### A Concrete Scenario of Interaction Imagine a future Fedwiki ecosystem that incorporates Federated AI: * You write pages in your personal Fedwiki about your research on, say, "Urban Bird Migration." * A Federated AI model, trained across hundreds of other naturalists' Fedwikis, runs locally on your node. * As you type a new page, the AI **suggests relevant links or citations** not just from your own wiki, but from other wikis in the federation you follow. It does this based on what it has learned *decentrally* from all those other wikis, **without any of the raw page content from those wikis ever being sent to a central server**. * The AI helps you discover connections and build knowledge in a privacy-preserving, user-empowering way that is perfectly aligned with the Fedwiki's original ethos. **In summary:** **Federated AI and the Fedwiki are kindred spirits in the movement towards a more resilient, private, and user-sovereign web.** They both demonstrate how powerful, collective intelligence can emerge without the need for centralization and the data exploitation that typically comes with it. The Fedwiki provides the architecture for decentralized knowledge, while Federated AI provides a potential mechanism for decentralized, privacy-respecting intelligence on top of that architecture.

connections

patterning

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