Adaptive Aggregation for Fair Federated Learning: Achieving Client-Level Fairness through Sequential Decision Making

Concepts de base

This paper proposes AAggFF, a novel framework leveraging online convex optimization (OCO) to improve client-level fairness in federated learning by adaptively adjusting mixing coefficients based on client performance feedback.

Résumé

• Bibliographic Information: Hahn, S-J., Kim, G-S., & Lee, J. (2024). Pursuing Overall Welfare in Federated Learning through Sequential Decision Making. Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024.

• Research Objective: This paper addresses the challenge of client-level fairness in federated learning, where a single global model may not perform equally well for all clients due to data heterogeneity. The authors aim to develop a fair FL algorithm that achieves more uniform performance across clients.

• Methodology: The authors propose AAggFF, a framework that unifies existing fairness-aware adaptive aggregation methods under the umbrella of online convex optimization (OCO). They frame the server's task of determining mixing coefficients as a sequential decision-making process. Two specific algorithms are proposed: AAggFF-S for cross-silo FL (using Online Newton Step) and AAggFF-D for cross-device FL (using a variant of Exponentiated Gradient adapted to FTRL and a doubly robust estimator for handling client sampling). Both algorithms incorporate a CDF-driven response transformation to ensure bounded local loss signals for theoretical guarantees.

• Key Findings: The paper provides theoretical analysis proving sublinear regret upper bounds for both AAggFF-S (O(L∞K log T)) and AAggFF-D (O(L∞√T log K)). Empirical evaluations on various datasets demonstrate that AAggFF consistently improves the performance of the worst-performing clients while maintaining competitive average performance compared to existing fair FL algorithms. The reduced Gini coefficient further confirms the effectiveness of AAggFF in achieving client-level fairness.

• Main Conclusions: This work highlights the potential of employing OCO principles for designing fair FL algorithms. AAggFF, with its two specialized variants, provides a practical and theoretically sound approach to mitigate performance disparities across clients in federated learning.

• Significance: This research contributes significantly to the field of fair federated learning by introducing a novel framework grounded in OCO. The proposed algorithms and theoretical analysis provide valuable insights for developing future fair FL solutions.

• Limitations and Future Research: While AAggFF demonstrates promising results, further investigation into handling more complex data heterogeneity scenarios and exploring alternative OCO algorithms could further enhance fairness and efficiency.

Stats

The cross-silo setting involves a small number of clients (K) and a larger number of communication rounds (T), such as K = 20 institutions with T = 200 rounds.
The cross-device setting involves a massive number of clients (K) exceeding the number of communication rounds (T), such as K = 1.5 × 10^6 with T = 3,000 rounds.
In the cross-device setting, client sampling is necessary due to the large number of clients.
AAggFF-S achieves a regret upper bound of O(L∞K log T).
AAggFF-D achieves a regret upper bound of O(L∞√T log K).

Citations

"In traditional federated learning, a single global model cannot perform equally well for all clients."
"Our work reveals that existing fairness-aware aggregation strategies can be unified into an online convex optimization framework, in other words, a central server’s sequential decision making process."
"Extensive experiments demonstrate that the federated system equipped with AAggFF achieves better degree of client-level fairness than existing methods in both practical settings."

Idées clés tirées de

Pursuing Overall Welfare in Federated Learning through Sequential Decision Making

by Seok-Ju Hahn... à arxiv.org 11-19-2024

https://arxiv.org/pdf/2405.20821.pdf

Questions plus approfondies

How can AAggFF be adapted to address fairness concerns beyond performance metrics, such as fairness in resource allocation or privacy preservation during training?

AAggFF, primarily designed to address client-level fairness in terms of performance metrics, can be extended to tackle broader fairness concerns in federated learning:
1. Fairness in Resource Allocation:

Adaptive Client Sampling: Instead of uniform random sampling, prioritize clients with historically lower resource contributions (e.g., communication bandwidth, computation power) or those experiencing higher resource consumption costs. This can be integrated into AAggFF by modifying the client sampling probability (C) in AAggFF-D, incorporating resource usage metrics into the selection process.
Weighted Aggregation Based on Resource Contribution: Assign higher mixing coefficients to clients contributing more resources, acknowledging their efforts and potentially incentivizing further participation. This can be achieved by modifying the response vector (r(t)) in AAggFF to incorporate a resource contribution factor for each client.
2. Privacy Preservation During Training:

Differential Privacy (DP): Integrate DP mechanisms like Gaussian noise addition to the local updates or the aggregated global model. The level of noise can be adapted based on client-specific privacy requirements or sensitivity levels. This can be incorporated into AAggFF by adding calibrated noise to the local gradients or the aggregated global model update.
Secure Aggregation: Employ cryptographic techniques like secure multi-party computation to aggregate local updates without revealing individual client information. This ensures privacy-preserving aggregation while maintaining the fairness-aware weighting scheme of AAggFF.
Challenges and Considerations:

Trade-offs: Addressing multiple fairness dimensions simultaneously might introduce trade-offs. For instance, prioritizing resource-constrained clients could potentially impact the overall convergence speed or accuracy of the global model.
Metric Selection: Defining appropriate metrics to quantify fairness in resource allocation and privacy preservation is crucial. These metrics should be carefully chosen to reflect the specific fairness goals and constraints of the federated learning system.

Could focusing solely on the worst-performing clients negatively impact the overall performance of the global model in AAggFF, and how can this trade-off be balanced effectively?

Yes, excessively focusing on the worst-performing clients in AAggFF could potentially hinder the overall performance of the global model. This is known as the fairness-accuracy trade-off.
Potential Negative Impacts:

Bias Towards Outliers: Overemphasizing outliers (extremely poor-performing clients) might bias the global model towards their data distributions, sacrificing generalizability on the majority of clients.
Slower Convergence: Constantly adapting to the worst performers could lead to oscillations in the model updates, slowing down the convergence process.
Balancing the Trade-off:

Regularization: Introduce a regularization term in the AAggFF objective function to penalize excessive deviations from the average performance. This encourages a balance between improving the worst-case performance and maintaining overall accuracy.
Performance Thresholds: Instead of solely focusing on the absolute worst, set a performance threshold. Clients below this threshold receive priority, while those above it contribute to the global model with standard weighting.
Dynamic Adaptation: Adjust the emphasis on worst-performing clients dynamically. In early stages, prioritize overall accuracy, and gradually shift focus towards fairness as the model converges.
Multi-Objective Optimization: Formulate the problem as a multi-objective optimization, explicitly considering both average performance and fairness metrics. Techniques like Pareto optimization can be used to explore trade-off solutions.

If we view the evolution of client performance in federated learning as an emergent system, what insights from complexity theory can be applied to design more robust and fair learning algorithms?

Viewing client performance evolution in federated learning as an emergent system through the lens of complexity theory offers valuable insights for designing more robust and fair algorithms:
1. Self-Organization and Adaptation:

Decentralized Adaptation: Instead of relying solely on a central server, empower clients with local adaptation mechanisms. This allows them to adjust their learning processes based on their individual data distributions and performance, potentially leading to a more balanced and fair global model.
Feedback Loops: Design algorithms with feedback loops that propagate information about client performance back into the system. This allows for dynamic adjustments to the learning process, promoting self-organization towards a fairer outcome.
2. Network Effects and Interdependence:

Client Clustering: Analyze client performance patterns to identify clusters of similar behavior. This can inform the design of personalized aggregation schemes where clients within a cluster contribute more heavily to a shared model, improving fairness within those groups.
Influence Propagation: Model the influence of client updates on each other. This understanding can be used to mitigate the impact of dominant clients or amplify the contributions of under-represented ones, fostering a more equitable learning environment.
3. Robustness and Stability:

Diversity Promotion: Encourage diversity in client models and data distributions. This can enhance the robustness of the global model to adversarial attacks or shifts in data patterns, leading to a more stable and fair system.
Early Warning Signals: Monitor client performance for early warning signals of instability or unfairness. This allows for timely interventions to prevent cascading failures or exacerbate existing inequalities.
Challenges and Considerations:

Complexity and Scalability: Analyzing and modeling emergent behavior in large-scale federated learning systems can be computationally expensive. Efficient algorithms and techniques are needed to handle this complexity.
Interpretability: Understanding the emergent dynamics of federated learning systems can be challenging. Developing interpretable models and visualizations is crucial for gaining insights and designing effective interventions.