Table of Contents

Interface IAggregationStrategy<TModel>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Defines strategies for aggregating model updates from multiple clients in federated learning.

public interface IAggregationStrategy<TModel>

Type Parameters

TModel

The type of model being aggregated.

Remarks

This interface represents different methods for combining model updates from distributed clients into a single improved global model.

For Beginners: An aggregation strategy is like a voting system or consensus mechanism that decides how to combine different opinions into a single decision.

Think of aggregation strategies as different ways to combine contributions:

  • Simple average: Everyone's input counts equally
  • Weighted average: Some contributors' inputs count more based on criteria (data size, accuracy)
  • Robust methods: Ignore outliers or malicious contributions

For example, in a federated learning scenario with hospitals:

  • Hospital A has 10,000 patients: gets weight of 10,000
  • Hospital B has 5,000 patients: gets weight of 5,000
  • The aggregation strategy might weight Hospital A's updates more heavily

Different strategies handle different challenges:

  • FedAvg: Standard weighted averaging
  • FedProx: Handles clients with different update frequencies
  • Krum: Robust to Byzantine (malicious) clients
  • Median aggregation: Resistant to outliers

Methods

Aggregate(Dictionary<int, TModel>, Dictionary<int, double>)

Aggregates model updates from multiple clients into a single global model update.

TModel Aggregate(Dictionary<int, TModel> clientModels, Dictionary<int, double> clientWeights)

Parameters

clientModels Dictionary<int, TModel>

Dictionary mapping client IDs to their trained models.

clientWeights Dictionary<int, double>

Dictionary mapping client IDs to their aggregation weights (typically based on data size).

Returns

TModel

The aggregated global model.

Remarks

This method combines model updates from clients using the strategy's specific algorithm.

For Beginners: Aggregation is like combining multiple rough drafts of a document into one polished version that incorporates the best parts of each.

The aggregation process typically:

  1. Takes model updates (weight changes) from each client
  2. Considers the weight or importance of each client (based on data size, accuracy, etc.)
  3. Combines these updates using the strategy's algorithm
  4. Returns a single aggregated model that represents the collective improvement

For example with weighted averaging (FedAvg):

  • Client 1 (1000 samples): model update A
  • Client 2 (500 samples): model update B
  • Client 3 (1500 samples): model update C
  • Aggregated update = (1000A + 500B + 1500*C) / 3000

GetStrategyName()

Gets the name of the aggregation strategy.

string GetStrategyName()

Returns

string

A string describing the aggregation strategy (e.g., "FedAvg", "FedProx", "Krum").

Remarks

For Beginners: This helps identify which aggregation method is being used, useful for logging, debugging, and comparing different strategies.

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