Boosting Fairness and Robustness in Over-the-Air Federated Learning

核心概念

The author proposes an Over-the-Air federated learning algorithm to enhance fairness and robustness through minmax optimization, showcasing convergence to the optimal solution without reconstructing channel coefficients. The approach improves efficiency and privacy by leveraging a novel communication strategy.

摘要

The content introduces an Over-the-Air federated learning algorithm aiming at fairness and robustness through minmax optimization. It highlights the decentralized training of machine learning models using efficient communication strategies. The proposed algorithm eliminates the need for complex encoding-decoding schemes, enhancing both efficiency and privacy. By addressing challenges like heterogeneity in large-scale systems, the algorithm provides insights into improving performance while ensuring fairness. The numerical example demonstrates the effectiveness of the FedFAir algorithm compared to traditional approaches like FedAVG, showcasing faster execution and higher accuracy rates.

統計資料

Each agent i has its own local dataset represented by Di = {dn i }|Di| n=1.
The step size is chosen as η(k) = 0.1/(k+1)^0.6.
For large N systems, it takes N = 12 time slots per communication round for each agent to transmit its updated parameter vector.
The FedFAir algorithm achieves around 90% accuracy after approximately 5000 iterations.
The FedAVG algorithm achieves approximately 70 - 75% classification accuracy after 100000 iterations.

引述

"In this paper, we propose an Over-the-Air federated learning algorithm that aims to provide fairness and robustness through minmax optimization."
"The proposed approach does not require reconstructing channel coefficients by complex encoding-decoding schemes as opposed to state-of-the-art approaches."

從以下內容提煉的關鍵洞見

Boosting Fairness and Robustness in Over-the-Air Federated Learning

by Halil Yigit ... 於 arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04431.pdf

深入探究

How can federated learning algorithms adapt to handle malicious agents within a system?

Federated learning algorithms can incorporate various mechanisms to mitigate the impact of malicious agents. One approach is to implement robust aggregation techniques that are resilient to data poisoning attacks. This involves using secure and verifiable aggregation methods, such as Byzantine fault-tolerant algorithms, to detect and exclude outliers introduced by malicious agents.
Furthermore, federated learning systems can employ anomaly detection mechanisms to identify unusual behavior in the model updates provided by individual agents. By monitoring the consistency and quality of contributions from each agent, suspicious activities can be flagged for further investigation or exclusion.
Additionally, implementing privacy-preserving techniques like differential privacy can help protect sensitive information from being manipulated by malicious entities. By adding noise or perturbations to the data before sharing it with other participants, federated learning models become more robust against adversarial manipulation.

How can heterogeneous data distributions impact the performance of traditional federated learning algorithms?

Heterogeneous data distributions pose significant challenges for traditional federated learning algorithms due to disparities in local datasets across participating agents. When each agent's dataset follows a different distribution or exhibits varying characteristics, convergence towards a global model becomes challenging.
The presence of heterogeneous data distributions leads to issues such as domain shift and non-IID (non-identically distributed) data among agents. As a result, models trained on one subset of data may not generalize well when applied to another subset with distinct features or statistical properties.
Traditional federated learning algorithms rely on averaging updates from all participants equally, assuming homogeneous datasets across all nodes. In scenarios with heterogeneity, this uniform aggregation strategy may lead to suboptimal performance as models struggle to capture diverse patterns present in disparate datasets.
To address these challenges posed by heterogeneous data distributions, advanced techniques such as personalized or adaptive aggregation strategies can be employed. These methods tailor the contribution weightings based on individual agent performance or dataset characteristics, allowing for more effective knowledge transfer while accommodating diversity among participants.

How Over-the-Air computation impacts privacy concerns in large-scale networks?

Over-the-Air computation offers inherent advantages for addressing privacy concerns in large-scale networks during federated learning processes:

Privacy-Preserving Communication: By leveraging Over-the-Air transmission methods instead of direct communication channels between devices and central servers, sensitive information remains protected during data exchange. The use of wireless channels reduces exposure risks associated with centralized storage or processing hubs vulnerable to security breaches.

Data Anonymity: Since Over-the-Air computation does not require reconstructing channel coefficients through complex encoding-decoding schemes—unlike conventional approaches—it ensures anonymity for transmitted information without compromising accuracy or efficiency.

Channel Coefficient Uncertainty: With Over-the-Air communication protocols operating despite unknown channel coefficients at any given time point—eliminating the need for pre-processing efforts related to channel reconstruction—the algorithm enhances both time efficiency and resource utilization while maintaining user privacy.

4 .Resource Efficiency: The efficient utilization of bandwidth resources through orthogonal signal transmission minimizes interference effects common in large-scale network environments where multiple users transmit simultaneously over shared frequency bands.
5 .Enhanced Privacy Guarantees: Given that channel coefficients remain completely unknown throughout computations utilizing Over-the-Air methodologies—as opposed
to requiring prior knowledge about them—the algorithm inherently guarantees user privacy protection against potential breaches.
These factors collectively contribute towards strengthening privacy measures within large-scale networks adopting Federated Learning paradigms via Over-the-Air computation strategies.