Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging (1909.05125v1)

Abstract: Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federated Averaging, a novel algorithm for robust federated learning that is designed to detect failures, attacks, and bad updates provided by participants in a collaborative model. We propose a Hidden Markov Model to model and learn the quality of model updates provided by each participant during training. In contrast to existing robust federated learning schemes, we propose a robust aggregation rule that detects and discards bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency. Our experimental evaluation on 4 real datasets show that our algorithm is significantly more robust to faulty, noisy and malicious participants, whilst being computationally more efficient than other state-of-the-art robust federated learning methods such as Multi-KRUM and coordinate-wise median.

• The paper introduces Adaptive Federated Averaging (AFA), a novel algorithm designed to provide robustness against Byzantine failures, biased data, and poisoning attacks in federated learning.
• AFA enhances model aggregation by using adaptive weights based on client reliability estimates derived from a Hidden Markov Model and employs a blocking mechanism for persistently unreliable clients.
• Experiments demonstrate that AFA achieves resilience comparable to clean conditions, high detection rates for malicious clients, and superior computational efficiency compared to alternative methods like MKRUM and COMED.

Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging

The paper "Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging" by Luis Munoz-Gonzalez, Kenneth T. Co, and Emil C. Lupu introduces a novel algorithm, Adaptive Federated Averaging (AFA), designed to enhance the robustness of federated learning systems against Byzantine failures, biased datasets, and poisoning attacks. This research addresses the vulnerabilities of standard federated learning protocols, which are subject to significant disruptions from malicious participants.

Overview of Adaptive Federated Averaging

Adaptive Federated Averaging (AFA) employs a rigorous approach to model aggregation that effectively mitigates the risks posed by Byzantine clients — those participants whose contributions might be erroneous or adversarial. AFA differs from other robust federated learning techniques by using a combination of adaptive model averaging and a Hidden Markov Model (HMM) to estimate the reliability of updates provided by each participant. The algorithm emphasizes efficiency by selectively incorporating updates, thereby discarding those deemed harmful or unreliable at each training iteration.

Key components of AFA include:

• Robust Aggregation: AFA computes the aggregated model update by weighing the updates based on their estimated reliability and the amount of data they represent. It adapts to discard known bad updates using a similarity measure and statistical thresholds derived from the data's inherent distribution.
• Bayesian Estimation: The reliability of each client's contribution is modeled using an HMM, providing a probabilistic framework to assess and adjust the weight of each update in real time.
• Blocking Mechanism: The system identifies and blocks persistently unreliable clients, improving both computational efficiency and reducing communication overhead.

This approach contrasts with earlier methods such as Multi-KRUM (MKRUM) and coordinate-wise median (COMED), which may not consider the amount of data or the quality of updates when aggregating model parameters.

Experimental Results

The experimental setup includes trials on four datasets: MNIST, Fashion-MNIST, Spambase, and CIFAR-10, under various adversarial conditions. Notable findings include:

• AFA consistently achieves resilience with error rates comparable to clean, non-adversarial conditions across all tested scenarios.
• The algorithm demonstrated a high detection rate for bad clients, achieving near-perfect performance in most conditions and successfully blocking malicious clients within roughly 5 to 9 training iterations.
• In terms of computational efficiency, AFA significantly outperforms MKRUM and COMED, with aggregation times that are markedly shorter.

Implications and Future Directions

AFA's integration into federated learning systems potentially enhances both theoretical and practical aspects of distributed machine learning. This strengthened resilience directly impacts the larger adoption of federated learning across fields where data privacy and integrity are paramount, such as healthcare and finance. The ability to dynamically filter out bad actors while maintaining robust model accuracy provides a promising path forward.

Future research should explore extending AFA's methodologies to counteract more sophisticated adversarial attacks, including targeted poisoning and backdoor techniques. Additionally, further refinements in client characterization and detection strategies could bolster federated learning frameworks against increasingly complex threat landscapes in AI-driven environments.

In summary, AFA represents a significant step forward in protecting and preserving the trustworthiness of federated learning models against the challenges posed by Byzantine adversaries.