Assessing Client Contributions in Federated Learning: FLContrib Framework

核心概念

The author proposes the FLContrib framework to assess client contributions in federated learning by leveraging Shapley values. The approach aims to balance efficiency and accuracy in evaluating client contributions.

摘要

The content discusses the FLContrib framework for assessing client contributions in federated learning using Shapley values. It introduces a history-aware game-theoretic approach to evaluate client contributions over epochs, demonstrating a controlled trade-off between correctness and efficiency. The framework considers both server-sided and client-sided fairness criteria, optimizing the assessment process. Experimental results show that FLContrib achieves a well-balanced performance compared to existing methods in terms of computational time and estimation error. Additionally, the application of FLContrib in detecting dishonest clients showcases its potential for analyzing client intentions based on historic contributions.
Key Points:

Proposal of FLContrib framework for assessing client contributions in federated learning.
Introduction of history-aware game-theoretic approach for evaluation over epochs.
Consideration of server-sided and client-sided fairness criteria.
Experimental results demonstrating balanced performance compared to existing methods.
Application of FLContrib in detecting dishonest clients.

統計資料

"FL yields a Markovian training process where in each training epoch, a central server sends the previous global model to participating clients."
"We consider F as the global FL model trained in T training epochs."
"In each epoch t, 1 ≤ t ≤ T, a subset of clients I(t) ⊆ I are selected for training."

引述

"We propose a history-aware game-theoretic framework, called FLContrib, to assess client contributions when a subset of (potentially non-i.i.d.) clients participate in each epoch of FL training."
"FLContrib demonstrates a well balance between efficiency of computation vs. estimation accuracy."

從以下內容提煉的關鍵洞見

Don't Forget What I did?

by Bishwamittra... 於 arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07151.pdf

深入探究

How can the FLContrib framework be extended to assess client contributions in decentralized federated learning settings

To extend the FLContrib framework for assessing client contributions in decentralized federated learning settings, several adjustments and enhancements can be made.

Decentralized Communication: In a decentralized setting, communication between clients is crucial. FLContrib can incorporate mechanisms for clients to securely exchange information about their local models and gradients.

Consensus Algorithms: Implementing consensus algorithms like Federated Averaging or Byzantine Fault Tolerance can ensure that all clients reach an agreement on the global model updates.

Client Selection Strategies: Decentralized settings may involve dynamic client participation. FLContrib could include adaptive client selection strategies based on factors like network conditions, data quality, or computational resources.

Privacy-Preserving Techniques: Given the distributed nature of decentralized federated learning, integrating privacy-preserving techniques such as secure multi-party computation or homomorphic encryption into FLContrib would enhance data security.

Robustness Against Adversarial Attacks: Decentralized environments are more vulnerable to adversarial attacks. FLContrib could incorporate defense mechanisms against malicious behaviors from individual clients.

By incorporating these elements tailored to a decentralized setup, FLContrib can effectively assess client contributions in a distributed federated learning environment.

What are some potential challenges or limitations associated with using Shapley values for assessing client contributions

Using Shapley values for assessing client contributions in federated learning comes with certain challenges and limitations:

Computational Complexity: Calculating exact Shapley values involves evaluating marginal contributions across all possible permutations of players, leading to exponential time complexity as the number of participants increases.

Sampling Errors: Approximation methods like Monte Carlo sampling may introduce errors due to limited samples used for estimation.

Assumption Sensitivity: Shapley values rely on cooperative game theory assumptions that might not always hold true in real-world scenarios where player interactions are complex and dynamic.

Interpretability Issues: Interpreting Shapley values accurately requires a deep understanding of game theory concepts which may pose challenges for non-experts.

5Fairness Concerns: While Shapley values provide fairness by attributing value based on contribution, defining what constitutes fair distribution among participants can be subjective and context-dependent.

How might the concept of fairness evolve or adapt within the context of federated learning as frameworks like FLContrib become more prevalent

As frameworks like FLContrib become more prevalent in federated learning contexts, the concept of fairness is likely to evolve in several ways:
1Algorithmic Fairness: There will be an increased focus on ensuring that machine learning models trained using federated approaches are fair and unbiased towards different demographic groups represented by participating clients.
2Incentive Mechanisms: Fairness considerations will extend beyond just model performance metrics to encompass how incentives are allocated among participants based on their contributions while maintaining equity and transparency.
3Data Privacy & Security: Ensuring fairness also means safeguarding sensitive data shared by clients during collaborative training processes through robust privacy-preserving measures that uphold individual rights without compromising utility.
4Regulatory Compliance: With stricter regulations around data protection (e.g., GDPR), frameworks like FLContrib will need to adapt to ensure compliance with evolving legal requirements related to user consent, data ownership, and accountability.
These adaptations reflect a broader shift towards ethical AI practices within federated learning ecosystems as stakeholders increasingly prioritize fairness alongside performance metrics when designing collaborative ML systems."