Introducing a novel consensus mechanism for federated learning in adversarial environments. BFT-FedAvg++ ensures model integrity even when 33% of nodes are malicious or compromised.

1. Introduction

Federated Learning (FL) allows multiple participants to collaboratively train a model without sharing their local data. However, standard aggregation methods like FedAvg are highly susceptible to "poisoning attacks" from malicious nodes. In a decentralized mesh like Eschon, we must assume that a portion of the nodes may be Byzantine (malicious or faulty).

2. The Problem: Model Poisoning

In a typical FL setup, a central aggregator (or a group of peers) averages the weights received from clients. A single malicious client can send extreme weight updates that "steer" the model toward a specific bias or completely destroy its accuracy.

3. BFT-FedAvg++ Architecture

Our protocol introduces three layers of defense:

3.1 Multi-Dimensional Krum (M-Krum)

We use a modified version of the Krum selection algorithm that operates across multiple dimensions of the weight vector to identify and discard outliers that deviate significantly from the median direction of the honest majority.

3.2 Verifiable Random Functions (VRF) for Aggregator Selection

Aggregators are not static. We use a VRF to randomly and secretly select a set of nodes to perform the aggregation for each round. This prevents targeted attacks on the aggregation point.

3.3 Zero-Knowledge Range Proofs (ZKRP)

Nodes must provide a ZKRP alongside their update, proving that their weights fall within a "reasonable" range established by the previous global model state, without revealing the actual values.

4. Performance Benchmarks

Our tests on the Eschon Alpha mesh (1,400+ nodes) show:

Resilience: Maintained 94% accuracy with up to 30% malicious nodes.
Overhead: ZKRP adds approximately 40ms to the update cycle—a negligible cost for the security gained.

5. Implementation for Developers

The BFT-FedAvg++ logic is encapsulated within the Apex SDK's VerifiableAggregation module.

use apex_sdk::ai::federated::{BFTAggregator, ModelUpdate};

// Aggregating with Byzantine tolerance
let aggregator = BFTAggregator::init(protocol_v4);
let global_model = aggregator.aggregate(updates, 0.33).expect("Consensus Failed");

6. Conclusion

BFT-FedAvg++ provides the mathematical foundation for trustless AI training. By combining traditional BFT consensus principles with modern FL aggregation, we enable the creation of global intelligence that is resistant to sabotage.

Published by the Eschon Research Unit, Nairobi.

BFT-FedAvg++: Byzantine-Resilient Federated Aggregation