Multi-bandit Best Arm Identification Achieves Efficient Partner Selection for Sequential Support Network Learning

Identifying the best collaborators from a larger pool of candidates presents a significant challenge in many modern machine learning applications, requiring efficient evaluation of potential partnerships through complex and computationally demanding processes. András Antos, András Millinghoffer, and Péter Antal, from Budapest University of Technology and Economics and E-Group ICT Software Zrt., address this problem with a new framework called Sequential Support Network Learning, which aims to discover the most beneficial network of contributing partners. Their research introduces a novel model, the semi-overlapping multi-bandit problem, that accurately reflects how evaluating one partnership provides feedback relevant to multiple potential connections, allowing for more efficient learning. The team develops an advanced algorithm and establishes improved theoretical guarantees for identifying optimal support networks, demonstrating substantial gains in efficiency, particularly when dealing with overlapping candidate lists, and paving the way for advancements in areas like multi-task learning, federated learning, and multi-agent systems.

Federated Learning, Client Selection and Heterogeneity

Research in federated learning, a dominant theme in recent studies, explores numerous facets of this distributed machine learning approach. Investigations focus on client selection, determining which devices participate in each training round, using strategies based on combinatorial multi-armed bandits, context-awareness, energy constraints, relevance, and importance. A significant area of study addresses heterogeneity, dealing with differences in data distributions, system capabilities, and client preferences, including personalization and data partitioning techniques. Further research examines privacy-preserving methods, such as Bayesian Networks, and optimization algorithms like bilevel and compositional optimization to improve the training process.

Studies also explore coalition formation, where clients collaborate in groups, and multi-objective optimization, balancing accuracy and fairness. Multi-task learning is another significant theme, with investigations into task grouping, relationships, negative transfer, weighting, and shared representation learning. Bandit algorithms and active learning are also being applied, utilizing multi-armed and combinatorial bandits for model selection and exploration. Game theory, particularly coalitional game theory and credit assignment in multi-agent reinforcement learning, provides frameworks for understanding collaboration.

Less frequent topics include Monte Carlo Tree Search, privacy-preserving machine learning, learning curves, and importance resampling. A key observation is the convergence of federated learning with other techniques to address challenges in distributed, heterogeneous data. This approach utilizes a novel model, the semi-overlapping multi-(multi-armed) bandit (SOMMAB), to learn support networks from limited candidate lists, recognizing that evaluating one partner’s contribution often yields feedback relevant to multiple others due to inherent structural overlap. To identify optimal partnerships within the SOMMAB framework, the team engineered a generalized GapE algorithm, adapting existing techniques like Upper Confidence Bounds and Successive Rejects to handle interconnected bandits. Rigorous analysis reveals exponential error bounds that improve upon existing benchmarks for multi-bandit best-arm identification, demonstrating a reduction in the number of trials needed to identify optimal partnerships. The core of this work is the semi-overlapping multi-(multi-armed) bandit (SOMMAB) model, which recognizes that evaluating one partner’s contribution provides feedback relevant to multiple collaborators due to inherent structural overlap. The team developed a generalized GapE algorithm for SOMMABs and derived new exponential error bounds that improve upon existing benchmarks for multi-bandit best-arm identification. These bounds scale linearly with the degree of overlap between evaluations, meaning increased shared computation directly translates into reduced data requirements. The team demonstrates that the semi-overlapping multi-(multi-armed) bandit model effectively learns support networks from limited candidate lists, leveraging the fact that a single evaluation can provide unique feedback to multiple interconnected problems. A refined algorithm, building on existing ‘GapE’ methods, allows for accurate identification of optimal partners, and the researchers establish improved mathematical bounds defining the efficiency of this process. The significance of this work lies in its ability to scale efficiently with increasing complexity and overlap between individual tasks, achieving gains in sample complexity as shared evaluations provide more information. These findings have implications for machine learning applications, including multi-task learning, federated learning, and multi-agent systems.