Federated Learning
180+Privacy-preserving distributed training across edge devices
Train AI models across distributed clients without centralizing sensitive data. Complete federated learning framework with 9+ aggregation strategies, differential privacy, secure aggregation, and heterogeneous data handling. Perfect for healthcare, finance, and any privacy-sensitive application.
Aggregation Strategies
Combine model updates from distributed clients with different aggregation algorithms.
FedAvg
Federated Averaging - the foundational algorithm for federated learning.
FedProx
Proximal term for handling heterogeneous data and systems.
FedNova
Normalized averaging handling variable local computation.
Scaffold
Stochastic Controlled Averaging correcting client drift.
FedOpt / FedAdam
Server-side adaptive optimization with Adam/Yogi/Adagrad.
Privacy & Security
Protect individual client data with cryptographic privacy guarantees.
Differential Privacy
Add calibrated noise to gradients guaranteeing individual privacy.
Secure Aggregation
Cryptographic protocol ensuring server sees only aggregate updates.
Gradient Compression
Compress gradient updates reducing communication and potential leakage.
Infrastructure
Manage clients, coordinate rounds, and handle system heterogeneity.
Client Manager
Register, authenticate, and manage federated learning participants.
Server Coordinator
Orchestrate training rounds, model distribution, and aggregation.
Model Versioning
Track global model versions and client-specific checkpoints.
Client Selection
Smart sampling of clients per round based on data quality and availability.
Federated learning with AiModelBuilder
using AiDotNet;
// Train with federated learning using AiModelBuilder
var result = await new AiModelBuilder<float, float[], float>()
.ConfigureModel(new NeuralNetwork<float>(
inputSize: 784, hiddenSize: 128, outputSize: 10))
.ConfigureFederatedLearning(new FederatedOptions(
strategy: new FedAvg<float>(),
rounds: 50, minClients: 2,
privacyBudget: new DifferentialPrivacy(epsilon: 1.0)))
.ConfigureOptimizer(new AdamOptimizer<float>())
.BuildAsync(features, labels);
var prediction = result.Predict(newSample);Start building with Federated Learning
All 180+ implementations are included free under Apache 2.0.