Federated Learning Model Diversity and Backup for Disconnected Clients
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Solution Overview
Problem
Federated learning systems face inefficiencies and robustness issues due to data heterogeneity, distribution shifts, and communication unreliability, particularly in scenarios with hardware failures, leading to accuracy drops and resource mismanagement across clients with varying capabilities.
Innovation Solution
A novel federated learning framework that stores diverse learnable models on servers, allowing clients to select models based on their resources and train locally, with orthogonal regularization and client-side loss functions to enhance model diversity and robustness, and provides backup solutions for communication failures through model interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning trains a global model using multiple local datasets across distributed clients, then data privacy is preserved and centralized storage is avoided, but communication unreliability and client disconnections cause training interruptions and accuracy drops
Solution Approach 1:
The system pre-trains multiple diverse server-maintained models (SMMs) before client disconnection occurs. When a client disconnects, these pre-trained diverse models serve as backup solutions, cushioning the impact of communication failures and ensuring training continuity without requiring complex real-time recovery mechanisms.
Solution Approach 2:
Diverse server-maintained models act as intermediaries between the central server and disconnected clients. Instead of directly managing client disconnections, the system uses these intermediate model representations to transfer knowledge and continue training, simplifying the overall system architecture while maintaining reliability.
2Reliability
If the system maintains multiple diverse server-maintained models for robustness against disconnections, then training continuity is improved, but server computational resources and memory usage increase
Solution Approach 1:
Different clients receive different subsets of the diverse server-maintained models based on their specific needs, data characteristics, and resource capabilities. This local customization allows the system to maintain multiple diverse models on the server while distributing them selectively to clients, optimizing resource utilization and reducing overall storage requirements.
Solution Approach 2:
The system segments the multiple diverse models into different groups or subsets that can be distributed to different clients. This segmentation reduces the burden on any single client and optimizes server resource allocation by only storing and transmitting necessary model portions to each client based on their specific requirements.
3Adaptability or versatility
If clients with varying hardware capabilities all participate in federated learning, then system inclusivity and data diversity are improved, but performance disparities and resource mismanagement occur
Solution Approach 1:
The system assigns different diverse server-maintained models to clients based on their specific hardware capabilities, data characteristics, and resource availability. This local quality approach ensures that each client receives models optimized for their specific context, allowing clients with varying hardware capabilities to participate effectively without causing performance disparities or resource mismanagement.
Data Source
AI summary
Methods and systems for training neural networks with federated learning. Server-maintained machine learning models are sent from a server to a plurality of clients, yielding local machine learning models. At each client, the local machine learning models are trained with locally-stored data, stored locally at that respective client. Respective losses are determined and weights updated for each of the local machine learning models. Updated weights are transferred to the server for updating of the server-maintained machine learning models for training of those models. If one of the clients is disconnected or otherwise unable to receive the server-maintained models, that disconnected client can connect to neighboring clients, receiving the models from those neighboring clients, and training those models with the disconnected clients own locally-stored data.