Federated RAG Updates

Federated Learning is a decentralized machine learning paradigm where a global model is trained across multiple edge devices holding local data samples, without exchanging the data itself. Instead, devices compute model updates (e.g., gradients) locally and send only these updates to a central server for aggregation.

• Core Mechanism: Uses algorithms like Federated Averaging (FedAvg) to aggregate updates.
• Privacy Benefit: Raw user data never leaves the local device.
• Key Challenge: Managing statistical heterogeneity (non-IID data) across devices.
• Example: Google's Gboard uses federated learning to improve next-word prediction without uploading typed sentences.

Differential Privacy is a rigorous mathematical framework for quantifying and limiting the privacy loss incurred when an individual's data is included in a computation. In federated RAG, it is applied by adding calibrated statistical noise to the aggregated model updates or query results.

• Formal Guarantee: Provides an epsilon (ε)-delta (δ) privacy budget, ensuring an individual's participation does not significantly affect the algorithm's output.
• Application: Used to protect against membership inference attacks when sharing retrieval model gradients or index updates.
• Mechanism: Common techniques include the Gaussian or Laplace mechanism for adding noise.

Homomorphic Encryption is a form of encryption that allows computations to be performed directly on ciphertext, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. In federated systems, it enables secure aggregation.

• Use Case: A central server can combine encrypted model updates from devices without being able to decrypt any individual update.
• Types: Partially Homomorphic Encryption (PHE) supports one operation (e.g., addition), while Fully Homomorphic Encryption (FHE) supports both addition and multiplication but is computationally intensive.
• RAG Application: Could enable private similarity search over encrypted vector indices.

Edge AI refers to the deployment of machine learning models directly on end-user devices, sensors, or local gateways, rather than in centralized cloud data centers. This enables low-latency inference, reduced bandwidth usage, and enhanced data privacy.

• Hardware: Runs on Neural Processing Units (NPUs), microcontrollers, or mobile SoCs.
• Key Constraint: Severe limitations in memory, compute, and power (SWaP).
• Relation to Federated RAG: Provides the foundational deployment environment where local RAG models operate and generate the private updates used in the federated process.

Incremental Learning is the ability of a machine learning model to learn continuously from a stream of new data without retraining from scratch on the entire historical dataset. It must adapt to new information while mitigating catastrophic forgetting of previous knowledge.

• Contrast with Federated Learning: Incremental learning focuses on sequential data arrival over time on a single system, while federated learning handles spatial distribution across devices.
• RAG Application: Federated RAG updates are a form of distributed incremental learning for retrieval models and indices, where each device's local data stream contributes to global model evolution.

Secure Aggregation is a cryptographic protocol used in federated learning to ensure that a central server can compute the sum of model updates from many users without learning any individual user's update. It prevents the server from performing a model update inversion attack.

• Mechanism: Often employs multi-party computation (MPC) or masking techniques where users' updates are encrypted or obfuscated in a way that cancels out during aggregation.
• Critical for Scale: Essential for practical federated systems with thousands of participants.
• RAG Specificity: Applied to the aggregation of retrieval model parameter deltas or new embedding vectors for a shared index.