Hierarchical Federated Learning Framework: Flight, a Scalable and Flexible Solution for Complex Distributed Systems

Belangrijkste concepten

Flight is an open-source framework that enables the implementation of complex and hierarchical federated learning processes, supporting asynchronous aggregation and decoupling the control and data planes for efficient and scalable deployment.

Samenvatting

The content presents Flight, a novel federated learning (FL) framework that supports complex hierarchical multi-tier topologies, asynchronous aggregation, and decouples the control plane from the data plane.

Key highlights:

• Flight enables the definition of arbitrary hierarchical network topologies, going beyond the typical two-tier FL setup.
• It provides modular interfaces for control and data planes, allowing the use of robust compute and data-management frameworks like Globus Compute and ProxyStore for remote execution and data transfer.
• Flight supports both synchronous and asynchronous FL execution schemes, providing flexibility for diverse deployment scenarios.
• The evaluation shows that Flight scales beyond the state-of-the-art Flower framework, supporting up to 2048 concurrent devices, and can reduce FL makespan and communication overheads by leveraging hierarchical topologies.
• Flight enables efficient deployment of FL processes on remote devices by decoupling control and data planes, and provides a comprehensive set of abstractions for customizing FL algorithms and strategies.

Statistieken

"Federated Learning (FL) trains individual models directly where data reside (e.g., edge devices, IoT devices, mobile devices, and sensors)."
"Because no training data are communicated over the network in FL, it provides two key benefits: (i) reduced communication cost, assuming the size of the model weights are less than the training data; and (ii) enhanced data privacy."
"Hierarchical Federated Learning (HFL) aims to address the limitations of the typical two-tier FL setup by enabling multi-tier and hierarchical network topologies, where intermediate aggregators can produce aggregated models that are more regional in their context."

Citaten

"Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server."
"Unlike conventional deep learning, FL trains individual models directly where data reside (e.g., edge devices, IoT devices, mobile devices, and sensors)."
"Because no training data are communicated over the network in FL, it provides two key benefits: (i) reduced communication cost, assuming the size of the model weights are less than the training data; and (ii) enhanced data privacy."

Belangrijkste Inzichten Gedestilleerd Uit

Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning

by Nathaniel Hu... om arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16495.pdf

Diepere vragen

How can Flight's hierarchical FL model be extended to support more complex network topologies, such as directed acyclic graphs or arbitrary graphs, beyond the tree-based topologies considered in this work?

To extend Flight's hierarchical Federated Learning (FL) model to support more complex network topologies, such as directed acyclic graphs (DAGs) or arbitrary graphs, several modifications can be made to the existing architecture.

Graph Representation: The current implementation uses a tree structure, which inherently limits the relationships between nodes to a parent-child hierarchy. By adopting a more flexible graph representation, such as using NetworkX to define arbitrary directed graphs, Flight can accommodate multiple parents for nodes, allowing for more complex interactions and dependencies among workers and aggregators.

Job Scheduling and Execution: In a DAG, nodes can have multiple predecessors and successors, which necessitates a more sophisticated job scheduling mechanism. Flight can implement a topological sorting algorithm to determine the order of job execution based on dependencies. This would ensure that a node only begins processing once all its parent nodes have completed their tasks.

Dynamic Node Management: The ability to dynamically add or remove nodes from the graph during runtime can enhance the flexibility of the system. This could involve implementing a mechanism for nodes to register themselves with the Coordinator and update their status, allowing for real-time adjustments to the network topology based on device availability or performance.

Aggregation Strategies: The aggregation process would need to be adapted to handle multiple incoming updates from various nodes. This could involve developing new aggregation strategies that consider the contributions from multiple parents, potentially using weighted averages based on the reliability or performance of the contributing nodes.

Fault Tolerance: With more complex topologies, the system must be robust against node failures. Implementing redundancy and fallback mechanisms, such as reassigning tasks to alternative nodes in case of failure, would be crucial for maintaining the integrity of the FL process.

By incorporating these enhancements, Flight can effectively support more complex network topologies, enabling it to better model real-world distributed systems and improve the efficiency and scalability of federated learning processes.

What are the potential challenges and trade-offs in implementing asynchronous aggregation strategies in hierarchical topologies, and how can Flight be further extended to address them?

Implementing asynchronous aggregation strategies in hierarchical topologies presents several challenges and trade-offs:

Consistency and Convergence: One of the primary challenges is ensuring the consistency of the global model. In asynchronous FL, updates from workers may arrive at different times, leading to potential conflicts and inconsistencies in the model parameters. This can hinder convergence, as the global model may be updated with stale or outdated information. To address this, Flight could implement versioning for model parameters, allowing the system to track the freshness of updates and apply strategies to prioritize more recent updates.

Communication Overhead: Asynchronous aggregation can lead to increased communication overhead, as workers may send updates at different times, resulting in a higher frequency of communication between nodes. Flight can mitigate this by implementing a batching mechanism, where updates from workers are collected over a defined period before being sent to aggregators. This would reduce the number of communication events and improve overall efficiency.

Load Balancing: In hierarchical topologies, some aggregators may become bottlenecks if they receive too many updates simultaneously. Flight can extend its architecture to include load balancing mechanisms that distribute the workload more evenly across aggregators, potentially by dynamically assigning workers to different aggregators based on their current load.

Latency and Performance: Asynchronous strategies can introduce latency in the aggregation process, as the global model may not be updated until all relevant updates are received. Flight could implement a hybrid approach, where partial aggregations are performed as updates arrive, allowing for more frequent model updates while still maintaining a level of consistency.

Complexity of Implementation: The complexity of managing asynchronous updates increases with the number of nodes and the complexity of the topology. Flight can provide higher-level abstractions and utilities to simplify the implementation of asynchronous strategies, allowing users to focus on defining their FL processes without getting bogged down in the underlying complexities.

By addressing these challenges, Flight can enhance its support for asynchronous aggregation strategies in hierarchical topologies, improving the scalability and efficiency of federated learning processes.

Given the focus on privacy preservation in federated learning, how can Flight be integrated with advanced privacy-preserving techniques, such as differential privacy or secure multi-party computation, to provide end-to-end privacy guarantees for the federated learning process?

Integrating Flight with advanced privacy-preserving techniques, such as differential privacy (DP) and secure multi-party computation (SMPC), can significantly enhance the privacy guarantees of the federated learning process. Here are several strategies for achieving this integration:

Differential Privacy: Flight can incorporate differential privacy mechanisms at the worker level, where local models are trained. By adding noise to the model updates before they are sent to the aggregators, Flight can ensure that individual contributions remain private. This can be implemented by modifying the local training process to include a DP mechanism, such as the Laplace or Gaussian mechanism, which adds calibrated noise to the gradients or model parameters.

Secure Multi-Party Computation: To further enhance privacy, Flight can integrate SMPC protocols that allow multiple parties to jointly compute a function over their inputs while keeping those inputs private. This could be particularly useful during the aggregation phase, where aggregators can compute the average of model updates without ever seeing the individual updates. Flight can implement existing SMPC libraries or protocols, allowing aggregators to perform secure computations on encrypted data.

Homomorphic Encryption: Another approach is to use homomorphic encryption, which allows computations to be performed on encrypted data. Flight can integrate libraries that support homomorphic encryption, enabling workers to encrypt their model updates before sending them to the aggregators. The aggregators can then perform the necessary aggregation operations on the encrypted data, ensuring that the individual updates remain confidential throughout the process.

Privacy Auditing and Compliance: Flight can include features for auditing and compliance with privacy regulations, such as GDPR or HIPAA. This could involve logging and monitoring data access and processing activities, ensuring that all operations comply with established privacy standards. Additionally, Flight can provide users with tools to assess the privacy guarantees of their federated learning processes, helping them make informed decisions about the trade-offs between model performance and privacy.

User-Controlled Privacy Settings: Flight can empower users with configurable privacy settings, allowing them to choose the level of privacy they require for their specific use case. This could include options for adjusting the amount of noise added for differential privacy, selecting the type of encryption used, or determining the aggregation strategy that best balances privacy and performance.

By integrating these advanced privacy-preserving techniques, Flight can provide robust end-to-end privacy guarantees for federated learning processes, ensuring that sensitive data remains protected while still enabling effective model training and aggregation.